Where innovation meets insight. Explore expert perspectives, emerging tech trends, and real-world stories in coding, hiring, AI, and hackathons. Whether you're building, hiring, or learning — this is your go-to hub for sharp insights and practical knowledge from across the global developer ecosystem.
Featured and trending
Stay Ahead with the HackerEarth Blog
Dive into the latest in tech innovation, industry updates, and thought leadership. Discover what’s shaping the future — one post at a time.
Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.
From Machine Language to Natural Language
I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing c
The Promise and the Pitfalls
I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.
AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.
The Economic Impact
The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation. Seizing the Opportunity
Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.
This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.
Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.
In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers.
The ease and efficiency of virtual interviews, take home programming tests and remote coding challenges is transformative. Around 82% of companies use pre-employment assessments as reliable indicators of a candidate's skills and potential.
Online skill assessment tests have been proven to streamline technical hiring and enable recruiters to significantly reduce the time and cost to identify and hire top talent.
In the realm of online assessments, remote assessments have transformed the hiring landscape, boosting the speed and efficiency of screening and evaluating talent. On the flip side, candidates have learned how to use creative methods and AI tools to cheat in tests.
As it turns out, technology that makes hiring easier for recruiters and managers - is also their Achilles' heel.
Cheating in Online Assessments is a High Stakes Problem
With the proliferation of AI in recruitment, the conversation around cheating has come to the forefront, putting recruiters and hiring managers in a bit of a flux.
The problem becomes twofold - if finding the right talent can be a competitive advantage, the consequences of hiring the wrong one can be equally damaging and counter-productive.
As per Forbes, a wrong hire can cost a company around 30% of an employee's salary - not to mention, loss of precious productive hours and morale disruption.
The question that arises is - "Can organizations continue to leverage AI-driven tools for online assessments without compromising on the integrity of their hiring process? "
This article will discuss the common methods candidates use to outsmart online assessments. We will also dive deep into actionable steps that you can take to prevent cheating while delivering a positive candidate experience.
Common Cheating Tactics and How You Can Combat Them
Using ChatGPT and other AI tools to write code
Copy-pasting code using AI-based platforms and online code generators is one of common cheat codes in candidates' books. For tackling technical assessments, candidates conveniently use readily available tools like ChatGPT and GitHub.
Using these tools, candidates can easily generate solutions to solve common programming challenges such as:
Debugging code
Optimizing existing code
Writing problem-specific code from scratch
Ways to prevent it
Enable full-screen mode
Disable copy-and-paste functionality
Restrict tab switching outside of code editors
Use AI to detect code that has been copied and pasted
Enlist external help to complete the assessment
Candidates often seek out someone else to take the assessment on their behalf. In many cases, they also use screen sharing and remote collaboration tools for real-time assistance.
In extreme cases, some candidates might have an off-camera individual present in the same environment for help.
Ways to prevent it
Verify a candidate using video authentication
Restrict test access from specific IP addresses
Use online proctoring by taking snapshots of the candidate periodically
Use a 360 degree environment scan to ensure no unauthorized individual is present
Using multiple devices at the same time
Candidates attempting to cheat often rely on secondary devices such as a computer, tablet, notebook or a mobile phone hidden from the line of sight of their webcam.
By using multiple devices, candidates can look up information, search for solutions or simply augment their answers.
Ways to prevent it
Track mouse exit count to detect irregularities
Detect when a new device or peripheral is connected
Use network monitoring and scanning to detect any smart devices in proximity
Conduct a virtual whiteboard interview to monitor movements and gestures
Using remote desktop software and virtual machines
Tech-savvy candidates go to great lengths to cheat. Using virtual machines, candidates can search for answers using a secondary OS while their primary OS is being monitored.
Remote desktop software is another cheating technique which lets candidates give access to a third-person, allowing them to control their device.
With remote desktops, candidates can screen share the test window and use external help.
Ways to prevent it
Restrict access to virtual machines
AI-based proctoring for identifying malicious keystrokes
Use smart browsers to block candidates from using VMs
Future-proof Your Online Assessments With HackerEarth
HackerEarth's AI-powered online proctoring solution is a tested and proven way to outsmart cheating and take preventive measures at the right stage.
With HackerEarth's Smart Browser, recruiters can mitigate the threat of cheating and ensure their online assessments are accurate and trustworthy.
Secure, sealed-off testing environment
AI-enabled live test monitoring
Enterprise-grade, industry leading compliance
Built-in features to track, detect and flag cheating attempts
Boost your hiring efficiency and conduct reliable online assessments confidently with HackerEarth's revolutionary Smart Browser.
Former employees who return to work with the same organisation are essential assets. In talent acquisition, such employees are also termed as ‘Boomerang employees’. Former employees are valuable because they require the least training and onboarding because of their familiarity with the organization’s policies. Rehiring former employees by offering them more perks is a mark of a successful hiring process. This article will elaborate on the talent acquisition strategies for rehiring former employees, supported by a few real-life examples and best practices.
Why Should Organizations Consider Rehiring?
One of the best ways of ensuring quality hire with a low candidate turnover is to deploy employee retention programs like rehiring female professionals who wish to return to work after a career break. This gives former employees a chance to prove their expertise while ensuring them the organization’s faith in their skills and abilities. Besides, seeing former employees return to their old organizations encourages newly appointed employees to be more productive and contribute to the overall success of the organization they are working for. A few other benefits of rehiring old employees are listed below.
Reduced Hiring Costs
Hiring new talent incurs a few additional costs. For example, tasks such as sourcing resumes of potential candidates, reaching out to them, conducting interviews and screenings costs money to the HR department. Hiring former employees cuts down these costs and aids a seamless transition process for them.
Faster Onboarding
Since boomerang employees are well acquainted with the company’s onboarding process, they don’t have to undergo the entire exercise. A quick, one-day session informing them of any recent changes in the company’s work policies is sufficient to onboard them.
Retention of Knowledge
As a former employee, rehired executives have knowledge of the previous workflows and insights from working on former projects. This can be valuable in optimizing a current project. They bring immense knowledge and experience with them which can be instrumental in driving new projects to success.Starbucks is a prime example of a company that has successfully leveraged boomerang employees. Howard Schultz, the company's CEO, left in 2000 but returned in 2008 during a critical time for the firm. His leadership was instrumental in revitalizing the brand amid financial challenges.
Best Practices for Rehiring Former Employees
Implementing best practices is the safest way to go about any operation. Hiring former employees can be a daunting task especially if it involves someone who was fired previously. It is important to draft certain policies around rehiring former employees. Here are a few of them that can help you to get started.
1. Create a Clear Rehire Policy
While considering rehiring a former employee, it is essential to go through data indicating the reason why they had to leave in the first place. Any offer being offered must supersede their previous offer while marking clear boundaries to maintain work ethics. Offer a fair compensation that justifies their skills and abilities which can be major contributors to the success of the organization. A well-defined policy not only streamlines the rehiring process but also promotes fairness within the organization.
2. Conduct Thorough Exit Interviews
Exit interviews provide valuable insights into why employees leave and can help maintain relationships for potential future rehires. Key aspects to cover include:
Reasons for departure.
Conditions under which they might consider returning.
Feedback on organizational practices.
Keeping lines of communication open during these discussions can foster goodwill and encourage former employees to consider returning when the time is right.
3. Maintain Connections with Alumni
Creating and maintaining an alumni association must be an integral part of HR strategies. This exercise ensures that the HR department can find former employees in times of dire need and indicates to former employees how the organization is vested in their lives even after they have left them. This gesture fosters a feeling of goodwill and gratitude among former hires. Alumni networks and social media groups help former employees stay in touch with each other, thus improving their interpersonal communication.Research indicates that about 15% of rehired employees return because they maintained connections with their former employers.
4. Assess Current Needs Before Reaching Out
Before reaching out to former employees, assess all viable options and list out the reasons why rehiring is inevitable. Consider:
Changes in job responsibilities since their departure.
Skills or experiences gained by other team members during their absence.
It is essential to understand how the presence of a boomerang employee can be instrumental in solving professional crises before contacting them. It is also important to consider their present circumstances.
5. Initiate an Honest Conversation
When you get in touch with a former employee, it is important to understand their perspective on the job being offered. Make them feel heard and empathize with any difficult situations they may have had to face during their time in the organization. Understand why they would consider rejoining the company. These steps indicate that you truly care about them and fosters a certain level of trust between them and the organization which can motivate them to rejoin with a positive attitude.
6. Implement a Reboarding Program
When a former employee rejoins, HR departments must ensure a robust reboarding exercise is conducted to update them about any changes within the organization regarding the work policies and culture changes, training them about any new tools or systems that were deployed during their absence and allowing them time to reconnect with old team members or acquaint with new ones.
7. Make Them Feel Welcome
Creating a welcoming environment is essential for helping returning employees adjust smoothly. Consider:
Organizing team lunches or social events during their first week.
Assigning a mentor or buddy from their previous team to help them reacclimate.
Providing resources that facilitate learning about any organizational changes.
A positive onboarding experience reinforces their decision to return and fosters loyalty.
Real-Life Examples of Successful Rehiring
Several companies have successfully implemented these strategies:
IBM: The tech giant has embraced boomerang hiring by actively reaching out to former employees who possess critical skills in emerging technologies. IBM has found that these individuals often bring fresh perspectives that contribute significantly to innovation7.
Zappos: Known for its strong company culture, Zappos maintains an alumni network that keeps former employees engaged with the brand. This connection has led to numerous successful rehiring instances, enhancing both morale and productivity within teams6.
Conclusion
Rehiring former employees can provide organizations with unique advantages, including reduced costs, quicker onboarding, and retained knowledge. By implementing strategic practices—such as creating clear policies, maintaining connections, assessing current needs, and fostering welcoming environments—companies can effectively tap into this valuable talent pool.
As organizations continue navigating an ever-changing workforce landscape, embracing boomerang employees may be key to building resilient teams equipped for future challenges. By recognizing the potential benefits and following best practices outlined above, businesses can create a robust strategy for rehiring that enhances both employee satisfaction and organizational performance.
Tech Hiring Insights
HackerEarth Blogs
Gain insights to optimize your developer recruitment process.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
With less than 3 days to go, this script is meant to help beginners with feisty ideas, machine learning workflow and motivation for ongoing machine learning challenge.Here's a quick workflow of what I've done below:
Load data and explore
Data Pre-processing
Dropped Features
One Hot Encoding
Feature Engineering
Model Training
Good Luck!Note: For more feature engineering ideas, spend time on exploring data by loan_status variable. For categorical vs categorical data, create dodged bar plots. For categorical vs continuous data, create density plots and use fill=as.factor(loan_status).
To help the community, feel free to contribute the equivalent python / C ++ script in the comments below.
Update: You can get python script for this solution from Jin Cong Ho's comment below.
Silicon Valley has been upbeat about Virtual Reality and Augmented Reality for a while now. But it appears that USA is not the only one riding this wave. Rest of the world, including India, has been creating a stir in these fields as well.
Although it is hard to predict how many Oculus devices or Google Cardboards are out there in the hands of consumers in this part of the world, India is doing a lot with different forms of Mixed Reality.
2016 Summer batch YCombinator startup Innov8 chief, Dr. Ritesh Malik, a serial entrepreneur and investor, has already touched base with Augmented Reality four years ago with Alive.Alive App, now acquired by a leading Indian media publication company – Times of India (TOI), allows its readers to access videos, pictures, and polls related to select news items appearing in the newspaper, just by focusing the phone camera on any content in the newspaper carrying the Alive logo.
With a $2.5 million funding round raised last year by Round Glass Partners, national award-winning director Anand Gandhi pioneered storytelling in VR by means of his new venture Memesys Culture Labs. The company has successfully been able to tap into Bollywood with its VR production arm,ElseVR. A behind-the-scenes promo of Dangal (a massive blockbuster movie from last year) was shot in 360 degrees by ElseVR and captured over 2 million views.
More recently,AutoVRse came out with its Virtual Automobile Showroom. It is an interactive experience that enables customers to explore and customize their cars in real-time in a virtual environment. The company has installed systems at a number of showrooms, kiosks, and retail outlets, giving customers a surreal experience of the look and feel of its vehicle-to-be before actually making the purchase.
Imaginate Software Labs is another AR/VR-based technology enterprise that offers innovative visualization products and services. The company largely works with enterprises, helping them build their own products in this category, and has recently raised a $500K round to boost operations. Imaginate also boasts of products developed in-house such as Dressy, a virtual fitting room.
GridRaster is an interesting company because the products it sells are technology products. The company is primarily based out of Palo Alto, California, but has its development office in Bengaluru and has already raised close to $1.65 million. GridRaster provides the infrastructure layer to overcome device limitations – compute and battery, by intelligently harnessing the computing power and providing a centralized cloud to power compelling VR/AR experiences. The solution provides high-performance graphics with ultra-low latency while hugely improving battery performance. The technology drives mass adoption of VR/AR by dramatically improving the reach of exciting content and bringing breathtaking, new experiences to users.
Adapting to this explosion of startups in Mixed Reality and a maturing developer ecosystem, a number of US tech accelerators and incubators are now focused on enrolling more Indian startups in their upcoming batches. Lowe’s, a home improvement retailer based in the U.S., opened its first startup accelerator, Lowe’s Innovation Lab, in the heart of the technology hub of India, Bengaluru, and is already home to many Virtual Reality startups. Target, Tesco, and Walmart Labs are other U.S. retail giants also focused on bringing more startups to the Mixed Reality space in their own incubation centers set up in India.
At a recent event hosted by Lowe’s Innovation Labs in association with VR Collective, many spokespersons from the above-mentioned retail giants were present, and all of them unanimously agreed that retail is going to be massively disrupted by Mixed Reality. The plan is to win back their long-losing market share to E-Commerce by placing important bets in Mixed Reality.
Other cool Indian startups in Mixed Reality
SmartVizX recently secured $500,000 seed capital to disrupt the architecture, engineering, and construction industry.
G for Gestures, a gesture recognition technology and VR integrated product, is disrupting the hospitality sector.
PlayShifu, run by Vivek Goyal (Stanford University Graduate of Business alumni), is another Indian startup making education fun for kids through a range of products in Augmented Reality.
House of Blue Beans is a startup revolutionizing the home design industry with its immersive application ‘Roomstyler’. Roomstyler is a VR-based consumer experience that allows designers and customers to collaborate on a visual platform to make the home-designing process extremely effective. It is one of the startups enrolled in the latest batch of Lowe’s Innovation Labs.
Developer Ecosystem
Developers are already driving the mobile gaming ecosystem in India. It shouldn’t take long for developers to turn their attention to building more applications for Augmented and Virtual Reality as the consumer groups get flooded with more devices.
Gaming engines like Unity3d and Unreal Engine that drive the Mixed Reality applications are popular with the tech talent as the same tools are used for developing mobile games.
It should not be surprising to see a multitude of AR/VR applications coming out soon from India as the ecosystem matures.
Recently, Myntra came up with its first brick-and-mortar retail outlet in Indiranagar, Bengaluru. Ananth Narayanan, CEO of Myntra and Jabong, says, “The goal is not to drive sales – but to engage with the customer and as you’d expect from us, we’re using a lot of technology to do just that. We’ve got VR zone, multi-touch screens, full-screen displays that mimic the Myntra app, and selfie zones.”
The VR zone is placed with 4 Samsung Gear headsets which reels a 360-degree video showcasing the “Roadster life”. You can see around with a band playing on the road, airplanes rushing overhead, and bikers zooming past. Although it can seem a little out of place for the store, it gives a nice touch and indicates the interests of the established players.
Byju’s Classes is also ramping up its development by hiring Unity engineers proficient in 360-video streaming, indicating its interest in including some of its content for VR as well. This is in sync with the future of learning and Byju’s Classes is moving forward with an initiative to drive experiential learning.
The space is heating up, with a lot of developer interest and institutional focus to discover the next big thing in Virtual and Augmented Reality.
What will you build next??
Come and Register at the UnitedByHcl hackathon. Happy Mixing Reality!!
The bot challenges on HackerEarth have so far all been for two players, with perfect information and no randomness. As such, there are primarily two effective strategies for building bots, which are covered in this tutorial.
Alpha-beta Pruning
This intuitive approach builds on the idea that the best move is one that puts your opponent in the worst position. By extending this logic several moves into the future, we arrive at the Minimax or Negamax algorithm.
function negamax(state, depth):
if depth == 0:
return [state.evaluate(), null]
bestScore = -infinity
for move in state.getMoves():
moveScore = -negamax(state.makeMove(move), depth-1)[0]
if moveScore > bestScore:
bestScore = moveScore
bestMove = move
return [bestScore, bestMove]
Evaluation Functions
An evaluation function estimates how favorable a position is. It returns a large positive value if good for player A, and a negative value if unfavorable. For example, in Reversi - Bot Challenge #5, a simple evaluation function could compute the number of valid moves for A minus those for B. More advanced functions could consider positional advantages like corners.
While complex evaluation functions offer more accuracy, they are harder to write, debug, tune, and evaluate quickly, possibly reducing search depth. It's best to start simple and improve incrementally, testing changes thoroughly.
Pruning
Alpha-beta pruning improves Minimax by skipping branches that won't affect the final decision. For instance, if move m1 leads to a best-case outcome of x1, and move m2 has at least one possible counter resulting in worse than x1, we can discard m2 early without further evaluation.
function alphabeta(state, depth, alpha, beta):
if depth == 0:
return [state.evaluate(), null]
bestScore = -infinity
for move in state.getMoves():
moveScore = -alphabeta(state.makeMove(move), depth-1, -beta, -alpha)[0]
if moveScore > bestScore:
bestScore = moveScore
bestMove = move
alpha = max(alpha, moveScore)
if alpha >= beta:
break
return [bestScore, bestMove]
Alpha-beta pruning is most effective when the best moves are searched first. Optimal ordering can double the search depth compared to Minimax. Even with random move ordering, it's significantly more efficient.
Monte Carlo Tree Search (MCTS)
MCTS is another popular strategy and doesn't require an evaluation function. Instead, it uses random simulations to estimate move quality. Each simulation has two phases: an informed selection phase followed by random moves.
To guide move selection, MCTS uses a formula balancing exploration and exploitation. It favors moves with high win ratios but ensures all moves are eventually explored:
function MCTS(state):
if state.numberOfSimulations == 0:
play rest of game randomly
state.numberOfSimulations += 1
if current player won:
return "won"
else:
state.wonGames += 1
return "lost"
bestScore = -infinity
for move in state.getMoves():
childState = state.makeMove(move)
if childState.numberOfSimulations == 0:
moveScore = infinity
else:
moveScore = (childState.wonGames / childState.numberOfSimulations) +
sqrt(2 * log(state.numberOfSimulations) / childState.numberOfSimulations)
if moveScore > bestScore:
bestScore = moveScore
bestMove = move
simulationResult = MCTS(state.makeMove(bestMove))
state.numberOfSimulations += 1
if simulationResult == "won":
state.wonGames += 1
return "lost"
else:
return "won"
After running MCTS from the root many times, pick the move with the best win ratio or the most simulations. MCTS is better for games with complex dynamics (e.g., Go), where good evaluation functions are hard to design. Alpha-beta is preferred when strong evaluation heuristics are available.
Conclusion
This tutorial introduced alpha-beta pruning and Monte Carlo Tree Search, the two main bot strategies in two-player games. Many improvements exist for both, particularly alpha-beta pruning. For more, explore the Chess Programming Wiki, which contains techniques applicable across many games.
With the beginning of new year, every startup’s open mailing list starts receiving emails from graduate students for summer internship. I have read hundreds of applications that follow a generic template, somewhat like this:
Dear Sir/Ma'am,
My name is and I am currently pursuing my in from . I am well-versed with C/C++ and have started learning Java. I’m also learning about Android and iOS apps. I have good knowledge of HTML, CSS, JS too.
If given chance, I’ll give my 100% at work. I am confident that I will be a valuable asset to your team.
Please find the attachment of my CV
Kind regards,
Seems like a decent email, however, there’s one major red flag about this application. The applicant is not talking anything about the startup he is applying to and how he will benefit them. This shows that the applicant has not researched about the company's business and engineering. So, how to write an application email for Software Engineering Internships at startups?
Find the right point of contact
Try to address the right person in the company. When you begin with “Hi” or “Hey there” or “Sir/Ma’am” you’re diffusing the responsibility of a reply, a lot of emails are lost in the haystack because everybody in the group thinks that it is not meant for them or someone else will reply. Connect with someone in the company and ask them for the right person to contact for the purpose. Address them in your application email.
Keep the intro short
Keep the subject line and introduction short. Begin with your name, major and institute. That’d be all. Next you should be talking about what you know about the company and how you both can benefit each other.
Talk more about what you can give to the company, less about what you’ll take away
Research about the company’s engineering. Many tech companies have engineering blogs. You’ll know what technologies they use. Now you have what technologies you should highlight in the application, only if you know them well.
Check out the open source projects of the company. Every good tech company knows what engineering tasks they are going to accomplish in the upcoming year. You can contribute to their public repositories. And then highlight this in your application.
Use the product or services offered by the company. Find all sorts of possible improvements and suggest solutions for them in your application email. Every user out there is pointing out problems to them via support tickets, so suggesting solutions for them is an important part.
Sure that’s going to make your application a little longer than usual, however, it will make you stand out among the crowd. Ideally, such an application should be addressed to one of the engineers at the company.
Be objective about why you’d be a good addition to the team
Terms like “I’ll be a good addition to the team”, “Fast learner” etc. are subjective in nature. You have to prove it objectively. This is why many companies have started giving away take-home projects. Candidates can work on it for a week and depending on the work accomplished, companies get a good idea of how the candidate will perform during the internship. Some companies think that the candidate can cheat by asking someone else to do the take-home project. So they keep the candidate for a week-long project on trial. To overcome all of this, you, as a candidate, can think of features or apps that you can build which could be of value to the company. You can also make use of any APIs exposed by the company. You can then highlight such contributions to the company in your application, which shows that you’re reliable because you have a good past record.
Have an online presence
LinkedIn and AngelList are extensively used by Talent Acquisition team because they are quite familiar with their profile format and features. A Github account with few repositories and regular commits helps you in two ways: It shows that 1. You know how to use git 2. You’re consistent at work. Engineers may check your code to judge you by its quality.
You may also create a portfolio for yourself. You can extensively highlight all your projects in the portfolio. You can also write tech blog posts about challenges you faced during some project and how you solved them. By thy way, if you’re good at communication (written or verbal) you will do well in any job in the world. And your application email is one of the things by which every company is going to judge your communication skills.
Résumé
Lastly, prepare a short résumé that highlights your strengths and modify it for the target company. The resume format suggested by careercup seems adequate. However, it is suggested to apply your creativity to make it stand out. People don’t spend more than 30 seconds to go through any resume. Make sure you make those 30 seconds count.
All of the above points are “DOs”, let’s have a look at few of the “DON'Ts”:
Don’t send blind emails in bulk in `to` or `cc` or `bcc` expecting at least one of them to respond. Target few companies and write personalized emails.
Don’t write a subject line longer than 5-8 words.
Don’t share your email format with your friend who is going to send the same email to the company by replacing a few things. You both will be rejected.
Don’t mention all the technologies you know, a company is not interested in what you know, they are interested in what you know that they use. Do your research well.
Don’t include subjective statements which cannot be proved like “I always give my 100%” and similar statements.
Don’t send reminder emails on your application email frequently. Give it a week for them to respond. They receive hundreds of emails every day.
Don’t say that “Review my CV and match it to open roles in your company”. It is candidate’s duty to target a particular position.
The startup hiring process includes resume filtering, phone screening, face-to-face interviews (plus take-home projects in some cases) and final interview with founders. Many good candidates are not able to get past the first hurdle. To all those candidates, next time you apply for an internship, use this post as a checklist. All the best!Till next time. Evíva!
Toward the end of 2016, Google DeepMind made their machine learning platform, DeepMind Lab, publicly available. Despite warnings from experts like Professor Stephen Hawking, Google’s decision to expose its software to other developers is part of a movement to further develop the capabilities of machine learning. They aren’t the only ones though. Facebook made its deep learning software public last year, and Elon Musk’s non-profit organization OpenAI released Universe, an open software platform that can be used to train AI systems. So, why have Google, OpenAI, and others made their platforms public, and how will this affect the adoption of machine learning?
Open source machine learning… Why?
The examples mentioned gives us a better picture. If you look closely, machine learning has always been open-source, and open R&D is the fundamental reason why machine learning is where it is today.
By making its machine learning platform available to the public, Google has validated an increased consciousness about its AI research. There are various advantages to making the software accessible such as finding new talent and capable startups to add to the Alphabet Inc. family. At the same time, developers can access DeepMind Lab, which will help address one of the key issues with ML research – the dearth of training environments. OpenAI has introduced a new virtual school for AI, Universe, which uses games and websites to train AI systems.
Making machine learning platforms publicly available is a much-needed move now.
5 advantages of open source in machine learning projects
1. Reproducing scientific results and fair comparison of algorithms: In machine learning, numerical simulations are frequently used to provide experimental validation and comparison of methods. Preferably, such a comparison between methods is based on a rigorous theoretical analysis. Open source tools and technology offer an opportunity to thoroughly conduct research using publicly available source code without depending on the vendor.
2. Quick bug finding and fixing: When you carry out machine learning projects using open source software, it becomes easy to detect and resolve bugs in the software.
3. Accelerate scientific development with low-cost, reusing methods: It is a known fact that scientific progress is always made based on existing methods and discovery, and the machine learning field is not an exception. The availability of open source technologies in machine learning can leverage existing resources for research and projects greatly.
4. Long-term availability and support: Whether it is an individual researcher, developer, or data scientist, open source might serve as a medium to ensure that everyone can use his/her research or discovery even after changing jobs. Thus, the chances of having long-term support are increased by releasing code under an open source license.
5. Faster adoption of Machine Learning by various industries: There are notable paradigms of the open source software that has supported the creation of multi-billion dollar machine learning companies and industries. The main reason for the adoption of machine learning by researchers and developers is the easy availability of high-quality open source implementations for free.
Accelerating the adoption curve of open source machine learning
The advancement of open-source machine learning will enable a steeper adoption curve of Artificial Intelligence thus encouraging developers and startups to work towards making AI smarter. The availability of software platforms is changing the way in which businesses develop AI, encouraging them to follow in the footsteps of Google, Facebook, and OpenAI’s by being more transparent about their research.
The shift toward open machine learning platforms is an important phase in ensuring that AI works for everyone, instead of just a handful of tech giants.
From my perspective, there are three reasons for tech giants to release open-source machine learning projects:
To hire engineers who have already started to engage with the open source community and have built an understanding via an open-source project
To control a machine learning platform that works best into their broader SDK or cloud-platform strategy
To grow the entire market because their market share has reached a saturation point
When a startup releases an open-source project, it triggers awareness, some of which gets converted into paid customers and recruitment. Startups, by their very definition, are trying to get a foothold in a specific market instead of growing an existing market. Open-source is frictionless. It costs nothing to serve another organic user and enable organizations to solve real problems, thus allowing the code to have a greater impact.
Instead of disrupting the startups that build proprietary technologies, open-source has given the world a taller pair of shoulders to stand on. One of the knock-on effects may be a shift in focus on where the value lies. With the commoditization of the entire AI technology stack, the focus shifts from core machine learning technologies to building the best models–and this requires a vast amount of data and domain experts to create and train the models. Large incumbent businesses with an existing network effect have a natural advantage.
Best frameworks in open source machine learning
There is a wide range of open source machine learning frameworks available in the market, which enable machine learning engineers to:
Build, implement and maintain machine learning systems
Generate new projects
Create new impactful machine learning systems
Some of the important frameworks include:
Apache Singa is a general, distributed, deep-learning platform for training big deep-learning models over large datasets. It is designed with an instinctive programming model based on layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users.
Shogun is among the oldest and most revered machine learning libraries. Shogun was created in 1999 and written in C++, but isn’t limited to working in C++. Thanks to the SWIG library, Shogun can be used in languages and environments such as:
Java
Python
C#
Ruby
R
Lua
Octave
Matlab
Shogun is designed for unified large-scale learning for a broad range of feature types and learning settings, like classification, regression, dimensionality reduction, clustering, etc. It contains several exclusive state-of-the-art algorithms, such as a wealth of efficient SVM implementations, multiple kernel learning, kernel hypothesis testing, Krylov methods, etc.
TensorFlow is an open source software library for numerical computation using data flow graphs. TensorFlow performs numerical computations using data flow graphs. These elaborate the mathematical computations with a directed graph of nodes and edges. Nodes implement mathematical operations and can also represent endpoints to feed in data, push out results or read/write persistent variables. Edges describe the input/output relationships between nodes. Data edges carry dynamically-sized multi-dimensional data arrays or tensors
Scikit-Learn leverages Python’s breadth by building on top of several existing Python packages — NumPy, SciPy, and matplotlib — for math and science work. The resulting libraries can be used either for interactive “workbench” applications or be embedded into other software and reused. The kit is available under a BSD license, and therefore, it’s fully open and reusable. Scikit-learn includes tools for many of the standard machine-learning tasks (such as clustering, classification, regression, etc.). Since scikit-learn was developed by a large community of developers and machine-learning experts, promising new techniques tend to be included in short order.
MLlib (Spark) is Apache Spark’s machine learning library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs. Spark MLlib is regarded as a distributed machine learning framework on top of the Spark Core which, mainly due to the distributed memory-based Spark architecture, is almost nine times as fast as the disk-based implementation used by Apache Mahout.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. It connects to data that is stored in Amazon S3, Redshift, or RDS, and can run binary classification, multiclass categorization, or regression on the said data to create a model
Apache Mahout, is a free and open source project of the Apache Software Foundation. It’s goal is to develop free distributed or scalable machine learning algorithms for diverse areas like collaborative filtering, clustering and classification. Mahout provides Java libraries and Java collections for various kinds of mathematical operations. Apache Mahout is implemented on top of Apache Hadoop using the MapReduce paradigm. Once Big Data is stored on the Hadoop Distributed File System (HDFS), Mahout provides the data science tools to automatically find meaningful patterns in these Big Data sets thus turning this into ‘big information’ quickly and easily
The final say
Machine learning can indeed solve real scientific and technological problems with the help of open source tools. If machine learning is to solve real scientific and technological problems, the community needs to build on each other’s open source software tools. We believe that there is an urgent need for machine learning open source software, which will fulfill several concurrent roles, which include:
Better means for reproducing results
Mechanism for providing academic recognition for quality software implementations
Acceleration of the research process by allowing the standing on shoulders of others (not necessarily tech giants!)
This time we decided to figure out which are the top countries to work with, for programming enthusiasts making a living as developers, software engineers, or data analysts.
From my experience, English speakers can find the most jobs in the U.S. (West Coast, obviously), United Kingdom (London), Ireland(IT employers always ask how to hire workers from Ireland), Netherlands (Amsterdam), Switzerland, and Belgium.
New Zealand and Australia are pretty popular among developers who love the laid-back lifestyle.
But the scenarios change when we talk about non-English speaking nations.
Japan is growing exponentially; Russia and China have a huge culture of programming, and IT companies are growing rapidly in these countries; and India, Southeast Asian countries (Singapore and Indonesia), and South Korea (Seoul) are other popular and growing markets.
Often, the lower median salary is easier to stomach because of the lower cost of living.
What is important to understand that the definition of “best country” may not be categorical, and depends on a lot of people’s preferences.
To keep things fair we decided to dig up data from some popular sources to identify the best countries to work in for software engineers.
We listed these countries in order of their Happiness index and technological advancement in the field of IT over the years.
Top 10 countries forSoftware engineers / Developers/ Data Scientists to work
Switzerland
Canada
Australia
Netherlands
Germany
USA
Sweden
Denmark
Singapore
United Kingdom
You can read the detailed research below and other picks of top countries list based on various job profiles
Google Trends
Google Trends is a public web facility of Google Inc., based on Google Search, that shows how often a particular search term is entered relative to the total search volume across various regions of the world, and in various languages (Wikipedia).
The below graphs compares the popularity of programming languagesin order of Java, Python, PHP, C#, JavaScript, C++, C, Objective-C, R, Swift, Angular JS, Ruby, Perl, Matlab in each country respectively.
Which means Java and R are searched more often and in greater volume as compared to Swift and Angular Js in Denmark.
Would you like to get updates once a month on our latest articles? We won’t spam, we promise. Subscribe now to The HackerEarth Blog!
Denmark
Switzerland
Canada
Netherlands
New Zealand
Australia
Sweden
Austria
USA
Germany
Ireland
Singapore
United Kingdom
Argentina
France
Spain
Japan
China
Greece
India
Indeed.co
Indeed.co, available in 50 countries and 28 languages, is one of the most
high-traffic job websites in the United States and other countries.
Using country-specific search for the number of software engineers jobs listed on
Indeed, we found data which matched our previous research on Top programming languages
to learn.
While Java remained the favorite in all the top destinations. C, C++, and C# programmers are still in demand in these
nations, making them “evergreen” programming languages and famous among software engineers and developers.
In the U.S., China, India, and Japan, PHP developers have quite sought after.
The requirement of R programmers is higher in Switzerland, USA, India, and much more so in Germany and France.
Canada, Netherlands, UK, USA, India, and China clearly require MatLab skills.
If you are a Ruby developer, Japan needs you. But Canada gives first preference to Perl coders.
Median Salary – Programmer salary by country
What’s happiness without a handsome salary?
Hence, we listed the average salary for a particular job (Source – PayScale).
These values have been expressed in US dollars.
Switzerland, Sweden, Australia, and the United States have higher software engineersalaries than other countries.
A data scientist is one of the highest-paid jobs across the globe. Argentina pays PHP developers generously compared
to the country’s average pay for other IT skills.
France is looking for Java and front-end developers, paying them well for their skills.
Japan, Singapore, and, particularly China and India, offer fairly poor compensation
despite having a high requirement for skilled employees.
We understand that the quality of life, safety, cost of living, state taxes, commute cost, etc. are some of the other
major factors to be considered when deciding the top work destinations for a developer.
However, job listing, the popularity of the skill, median salary, and happiness index are equally important.
Get to know the experts behind our content. From industry leaders to tech enthusiasts, our authors share valuable insights, trends, and expertise to keep you informed and inspired.
Artificial Intelligence (AI) has permeated virtually every industry, transforming operations and interactions. The tech recruitment sector is no exception, and AI’s influence shapes the hiring processes in revolutionary ways. From leveraging AI-powered chatbots for preliminary candidate screenings to deploying machine learning algorithms for efficient resume parsing, AI leaves an indelible mark on tech hiring practices.
Yet, amidst these promising advancements, we must acknowledge the other side of the coin: AI’s potential malpractices, including the likelihood of cheating on assessments, issues around data privacy, and the risk of bias against minority groups.
The dark side of AI in tech recruitment
The introduction of AI in recruitment, while presenting significant opportunities, also brings with it certain drawbacks and vulnerabilities. Sophisticated technologies could enable candidates to cheat on assessments, misrepresent abilities and potential hiring mistakes. This could lead to hiring candidates with falsifying skills or qualifications, which can cause a series of negative effects like:
Reduced work quality: The work output might be sub-par if a candidate doesn’t genuinely possess the abilities they claimed to have.
Team disruptions: Other team members may have to pick up the slack, leading to resentment and decreased morale.
Rehiring costs: You might have to let go of such hires, resulting in additional costs for replacement.
Data privacy is another critical concern
Your company could be left exposed to significant risks if your AI recruiting software is not robust enough to protect sensitive employee information. The implications for an organization with insufficient data security could be severe such as:
Reputational damage: Breaches of sensitive employee data can damage your company’s reputation, making it harder to attract clients and talented employees in the future.
Legal consequences: Depending on the jurisdiction, you could face legal penalties, including hefty fines, for failing to protect sensitive data adequately.
Loss of trust: A data breach could undermine employee trust in your organization, leading to decreased morale and productivity.
Financial costs: Besides potential legal penalties, companies could also face direct financial losses from a data breach, including the costs of investigation, recovery, and measures to prevent future breaches.
Operational disruption: Depending on the extent of the breach, normal business operations could be disrupted, causing additional financial losses and damage to the organization’s reputation.
Let’s talk about the potential for bias in AI recruiting software
Perhaps the most critical issue of all is the potential for unconscious bias. The potential for bias in AI recruiting software stems from the fact that these systems learn from the data they are trained on. If the training data contains biases – for example, if it reflects a history of preferentially hiring individuals of a certain age, gender, or ethnicity – the AI system can learn and replicate these biases.
Even with unbiased data, if the AI’s algorithms are not designed to account for bias, they can inadvertently create it. For instance, a hiring algorithm that prioritizes candidates with more years of experience may inadvertently discriminate against younger candidates or those who have taken career breaks, such as for child-rearing or health reasons.
This replication and possible amplification of human prejudices can result in discriminatory hiring practices. If your organization’s AI-enabled hiring system is found to be biased, you could face legal action, fines, and penalties. Diversity is proven to enhance creativity, problem-solving, and decision-making. In contrast, bias in hiring can lead to a homogenous workforce, so its absence would likely result in a less innovative and less competitive organization.
When used correctly, AI in recruitment can take your hiring to the next level
How do you evaluate the appropriateness of using AI in hiring for your organization? Here are some strategies for navigating the AI revolution in HR. These steps include building support for AI adoption, identifying HR functions that can be integrated with AI, avoiding potential pitfalls of AI use in HR, collaborating with IT leaders, and so on.
Despite certain challenges, AI can significantly enhance tech recruitment processes when used effectively. AI-based recruitment tools can automate many manual recruiting tasks, such as resume screening and interview scheduling, freeing up time for recruiters to focus on more complex tasks. Furthermore, AI can improve the candidate’s experience by providing quick responses and personalized communications. The outcome is a more efficient, candidate-friendly process, which could lead to higher-quality hires.
Let’s look at several transformational possibilities chatbots can bring to human capital management for candidates and hiring teams. This includes automation and simplifying various tasks across domains such as recruiting, onboarding, core HR, absence management, benefits, performance management, and employee self-service resulting in the following:
For recruiters:
Improved efficiency and productivity: Chatbots can handle routine tasks like responding to common inquiries or arranging interviews. Thereby, providing you with more time to concentrate on tasks of strategic importance.
Enhanced candidate experience: With their ability to provide immediate responses, chatbots can make the application process more engaging and user-friendly.
Data and insights: Chatbots can collect and analyze data from your interactions with candidates. And provide valuable insights into candidate preferences and behavior.
Improved compliance: By consistently following predefined rules and guidelines, chatbots can help ensure that hiring processes are fair and compliant with relevant laws and regulations.
Cost saving: By automating routine tasks for recruiters, chatbots can help reduce the labor costs associated with hiring.
Additionally, candidates can leverage these AI-powered chatbots in a dialog flow manner to carry out various tasks. These tasks include the following:
Personalized greetings: By using a candidate’s name and other personal information, chatbots can create a friendly, personalized experience.
Job search: They can help candidates search for jobs based on specific criteria.
Create a candidate profile: These AI-powered chatbots can guide candidates through the process of creating a profile. Thus, making it easier for them to apply for jobs.
Upload resume: Chatbots can instruct candidates on uploading their resume, eliminating potential confusion.
Apply for a job: They can streamline the application process, making it easier and faster for candidates to apply for jobs.
Check application status: Chatbots can provide real-time updates on a candidate’s application status.
Schedule interviews: They can match candidate and interviewer availability to schedule interviews, simplifying the process.
For hiring managers:
These can also be utilized by your tech hiring teams for various purposes, such as:
Create requisition: Chatbots can guide hiring managers through the process of creating a job requisition.
Create offers: They can assist in generating job offers, ensuring all necessary information is included.
Access requisition and offers: Using chatbots can provide hiring managers with easy access to job requisitions and offers.
Check on onboarding tasks: Chatbots can help track onboarding tasks, ensuring nothing is missed.
Other AI recruiting technologies can also enhance the hiring process for candidates and hiring teams in the following ways:
For candidates:
Tailor-made resumes and cover letters using generative AI:Generative AI can help candidates create custom resumes and cover letters, increasing their chances of standing out.
Simplifying the application process: AI-powered recruiting tools can simplify the application process, allowing candidates to apply for jobs with just a few clicks.
Provide similar job recommendations: AI can analyze candidates’ skills, experiences, and preferences to recommend similar jobs they might be interested in.
For recruiters:
Find the best candidate: AI algorithms can analyze large amounts of data to help you identify the candidates most likely to succeed in a given role.
Extract key skills from candidate job applications: Save a significant amount of time and effort by using AI-based recruiting software to quickly analyze job applications to identify key skills, thereby, speeding up the screening process.
Take feedback from rejected candidates & share similar job recommendations: AI can collect feedback from rejected candidates for you to improve future hiring processes and recommend other suitable roles to the candidate.
These enhancements not only streamline the hiring process but also improve the quality of hires, reduce hiring biases, and improve the experience for everyone involved. The use of AI in hiring can indeed take it to the next level.
Where is AI in recruitment headed?
AI can dramatically reshape the recruitment landscape with the following key advancements:
1. Blockchain-based background verification:
Blockchain technology, renowned for its secure, transparent, and immutable nature, can revolutionize background checks. This process which can take anywhere from between a day to several weeks today for a single recruiter to do can be completed within a few clicks resulting in:
Streamlined screening process: Blockchain can store, manage, and share candidates’ credentials and work histories. Thereby speeding up the verification and screening process. This approach eliminates the need for manual background checks. And leads to freeing up a good amount of time for you to focus on more important tasks.
Enhanced trust and transparency: With blockchain, candidates, and employers can trust the validity of the information shared due to the nature of the technology. The cryptographic protection of blockchain ensures the data is tamper-proof, and decentralization provides transparency.
Improved data accuracy and reliability: Since the blockchain ledger is immutable, it enhances the accuracy and reliability of the data stored. This can minimize the risks associated with false information on candidates’ resumes.
Faster onboarding: A swift and reliable verification process means candidates can be onboarded more quickly. Thereby, improving the candidate experience and reducing the time-to-hire.
Expanded talent pool: With blockchain, it’s easier and quicker to verify the credentials of candidates globally, thereby widening the potential talent pool.
2. Immersive experiences using virtual reality (VR):
VR can provide immersive experiences that enhance various aspects of the tech recruitment process:
Interactive job previews: VR can allow potential candidates to virtually “experience” a day i.e., life at your company. This provides a more accurate and engaging job preview than traditional job descriptions.
Virtual interviews and assessments: You can use VR to conduct virtual interviews or assessments. You can also evaluate candidates in a more interactive and immersive setting. This can be particularly useful for roles that require specific spatial or technical skills.
Virtual onboarding programs: New hires can take a virtual tour of the office, meet their colleagues, and get acquainted with their tasks, all before their first day. This can significantly enhance the onboarding experience and help new hires feel more prepared.
Immersive learning experiences: VR can provide realistic, immersive learning experiences for job-specific training or to enhance soft skills. These could be used during the recruitment process or for ongoing employee development.
To summarize, AI in recruitment is a double-edged sword, carrying both promise and potential problems. The key lies in how recruiters use this technology, leveraging its benefits while vigilantly managing its risks. AI isn’t likely to replace recruiters or HR teams in the near future. Instead, you should leverage this tool to positively impact the entire hiring lifecycle.
With the right balance and careful management, AI can streamline hiring processes. It can create better candidate experiences, and ultimately lead to better recruitment decisions. Recruiters should continually experiment with and explore generative AI. To devise creative solutions, resulting in more successful hiring and the perfect fit for every open role.
“Every hire is an investment for a company. A good hire will give you a higher ROI; if it is a bad hire, it will cost you a lot of time and money.”
Especially in tech hiring!
An effective tech recruitment process helps you attract the best talents, reduce hiring costs, and enhance company culture and reputation.
Businesses increasingly depend on technical knowledge to compete in today’s fast-paced, technologically driven world. Online platforms that provide technical recruiting solutions have popped up to assist companies in finding and employing top talent in response to this demand.
The two most well-known platforms in this field are HackerEarth and Mettl. To help businesses make wise choices for their technical employment requirements, we will compare these two platforms’ features, benefits, and limitations in this article.
This comparison of Mettl alternative, HackerEarth and Mettl itself, will offer helpful information to help you make the best decision, whether you’re a small company trying to expand your tech staff or a massive organization needing a simplified recruiting process.
HackerEarth
HackerEarth is based in San Francisco, USA, and offers enterprise software to aid companies with technical recruitment. Its services include remote video interviewing and technical skill assessments that are commonly used by organizations.
HackerEarth also provides a platform for developers to participate in coding challenges and hackathons. In addition, it provides tools for technical hiring such as coding tests, online interviews, and applicant management features. The hiring solutions provided by HackerEarth aid companies assess potential employees’ technical aptitude and select the best applicants for their specialized positions.
Mettl
Mettl, on the other hand, offers a range of assessment solutions for various industries, including IT, banking, healthcare, and retail. It provides online tests for coding, linguistic ability, and cognitive skills. The tests offered by Mettl assist employers find the best applicants for open positions and make data-driven recruiting choices. Additionally, Mettl provides solutions for personnel management and staff training and development.
Why should you go for HackerEarth over Mercer Mettl?
Because HackerEarth makes technical recruiting easy and fast, you must consider HackerEarth for technical competence evaluations and remote video interviews. It goes above and beyond to provide you with a full range of functions and guarantee the effectiveness of the questions in the database. Moreover, it is user-friendly and offers fantastic testing opportunities.
The coding assessments by HackerEarth guarantee the lowest time consumption and maximum efficiency. It provides a question bank of more than 17,000 coding-related questions and automated test development so that you can choose test questions as per the job role.
As a tech recruiter, you may need a clear understanding of a candidate’s skills. With HackerEarth’s code replay capability and insight-rich reporting on a developer’s performance, you can hire the right resource for your company.
Additionally, HackerEarth provides a more in-depth examination of your recruiting process so you can continuously enhance your coding exams and develop a hiring procedure that leads the industry.
HackerEarth and Mercer Mettl are the two well-known online tech assessment platforms that provide tools for managing and performing online examinations. We will examine the major areas where HackerEarth outperforms Mettl, thereby proving to be a great alternative to Mettl, in this comparison.
HackerEarth believes in upgrading itself and providing the most effortless navigation and solutions to recruiters and candidates.
HackerEarth provides various tools and capabilities to create and administer online tests, such as programming tests, multiple-choice questions, coding challenges, and more. The software also has remote proctoring, automatic evaluation, and plagiarism detection tools (like detecting the use of ChatGPT in coding assessments). On the other side, Mettl offers comparable functionality but has restricted capabilities for coding challenges and evaluations.
Test creation and administration
HackerEarth: It has a user-friendly interface that is simple to use and navigate. It makes it easy for recruiters to handle evaluations without zero technical know-how. The HackerEarth coding platform is also quite flexible and offers a variety of pre-built exams, including coding tests, aptitude tests, and domain-specific examinations. It has a rich library of 17,000+ questions across 900+ skills, which is fully accessible by the hiring team. Additionally, it allows you to create custom questions yourself or use the available question libraries.
Mettl: It can be challenging for a hiring manager to use Mettl efficiently since Mettl provides limited assessment and question libraries. Also, their team creates the test for them rather than giving access to hiring managers. This results in a higher turnaround time and reduces test customization possibilities since the request has to go back to the team, they have to make the changes, and so forth.
Reporting and analytics
HackerEarth: You may assess applicant performance and pinpoint areas for improvement with the help of HackerEarth’s full reporting and analytics tools. Its personalized dashboards, visualizations, and data exports simplify evaluating assessment results and real-time insights.
Most importantly, HackerEarth includes code quality scores in candidate performance reports, which lets you get a deeper insight into a candidate’s capabilities and make the correct hiring decision. Additionally, HackerEarth provides a health score index for each question in the library to help you add more accuracy to your assessments. The health score is based on parameters like degree of difficulty, choice of the programming language used, number of attempts over the past year, and so on.
Mettl: Mettl online assessment tool provides reporting and analytics. However, there may be only a few customization choices available. Also, Mettle does not provide code quality assurance which means hiring managers have to check the whole code manually. There is no option to leverage question-based analytics and Mettl does not include a health score index for its question library.
Adopting this platform may be challenging if you want highly customized reporting and analytics solutions.
HackerEarth: The security and privacy of user data are top priorities at HackerEarth. The platform protects data in transit and at rest using industry-standard encryption. Additionally, all user data is kept in secure, constantly monitored data centers with stringent access controls.
Along with these security measures, HackerEarth also provides IP limitations, role-based access controls, and multi-factor authentication. These features ensure that all activity is recorded and audited and that only authorized users can access sensitive data.
HackerEarth complies with several data privacy laws, such as GDPR and CCPA. The protection of candidate data is ensured by this compliance, which also enables businesses to fulfill their legal and regulatory responsibilities.
Mettl: The security and data privacy features of Mettl might not be as strong as those of HackerEarth. The platform does not provide the same selection of security measures, such as IP limitations or multi-factor authentication. Although the business asserts that it complies with GDPR and other laws, it cannot offer the same amount of accountability and transparency as other platforms.
Even though both HackerEarth and Mettl include security and data privacy measures, the Mettle alternative, HackerEarth’s platform is made to be more thorough, open, and legal. By doing this, businesses can better guarantee candidate data’s security and ability to fulfill legal and regulatory requirements.
Pricing and support
HackerEarth: To meet the demands of businesses of all sizes, HackerEarth offers a variety of customizable pricing options. The platform provides yearly and multi-year contracts in addition to a pay-as-you-go basis. You can select the price plan that best suits their demands regarding employment and budget.
HackerEarth offers chat customer support around the clock. The platform also provides a thorough knowledge base and documentation to assist users in getting started and troubleshooting problems.
Mettl: The lack of price information on Mettl’s website might make it challenging for businesses to decide whether the platform fits their budget. The organization also does not have a pay-as-you-go option, which might be problematic.
Mettl offers phone and emails customer assistance. However, the business website lacks information on support availability or response times. This lack of transparency may be an issue if you need prompt and efficient help.
User experience
HackerEarth: The interface on HackerEarth is designed to be simple for both recruiters and job seekers. As a result of the platform’s numerous adjustable choices for test creation and administration, you may design exams specifically suited to a job role. Additionally, the platform provides a selection of question types and test templates, making it simple to build and take exams effectively.
In terms of the candidate experience, HackerEarth provides a user-friendly interface that makes navigating the testing procedure straightforward and intuitive for applicants. As a result of the platform’s real-time feedback and scoring, applicants may feel more motivated and engaged during the testing process. The platform also provides several customization choices, like branding and message, which may assist recruiters in giving prospects a more exciting and tailored experience.
Mettl: The platform is intended to have a steeper learning curve than others and be more technical. It makes it challenging to rapidly and effectively construct exams and can be difficult for applicants unfamiliar with the platform due to its complex interface.
Additionally, Mettl does not provide real-time feedback or scoring, which might deter applicants from participating and being motivated by the testing process.
According to G2, HackerEarth and Mettl have 4.4 reviews out of 5. Users have also applauded HackerEarth’s customer service. Many agree that the staff members are friendly and quick to respond to any problems or queries. Overall, customer evaluations and feedback for HackerEarth point to the platform as simple to use. Both recruiters and applicants find it efficient.
Mettl has received mixed reviews from users, with some praising the platform for its features and functionality and others expressing frustration with its complex and technical interface.
May the best “brand” win!
Recruiting and selecting the ideal candidate demands a significant investment of time, attention, and effort.
This is where tech recruiting platforms like HackerEarth and Mettl have got you covered. They help streamline the whole process.Both HackerEarth and Mettl provide a wide variety of advanced features and capabilities for tech hiring.
We think HackerEarth is the superior choice. Especially, when contrasting the two platforms in terms of their salient characteristics and functioning. But, we may be biased!
So don’t take our word for it. Sign up for a free trial and check out HackerEarth’s offerings for yourself!
Let’s face it—cheating on tests is quite common. While technology has made a lot of things easier in tech recruiting, it
has also left the field wide open to malpractice. A 2020 report by ICAI shows
that 32% of undergraduate students have cheated in some form on an online test.
It’s human nature to want to bend
the rules a little bit. Which begs the question, how do you stay on top of cheating, plagiarism, and other forms of
malpractice during the assessment process?
How do you ensure that take-home assessments and remote interviews
stay authentic and credible? By relying on enhanced virtual supervision, of course!
HackerEarth Assessments has
always been one step ahead when it comes to remote proctoring which is able to capture the nuances of candidate
plagiarism. The recent advancements in technology (think generative AI) needed
more robust proctoring features, so we went ahead and built The HackerEarth Smart
Browser to ensure our assessments remain as foolproof as ever.
Presenting to you, the
latest HackerEarth proctoring fix - The Smart Browser
Our Smart Browser is the chocolatey version of a plain
donut when compared to a regular web browser. It is extra effective and comes packed with additional remote proctoring capabilities to
increase the quality of your screening assessments.
The chances of a candidate cheating on a HackerEarth technical assessment are virtually zero with
the latest features! Spilling all our secrets to show you why -
1. Sealed-off testing environment makes proctoring
simpler
To get started with
using the Smart Browser, enable the Smart Browser setting as shown above. This setting is available under the test
proctoring section on the test overview page.
As you can see, several other proctoring settings such as disabling
copy-paste, restricting candidates to full-screen mode, and logout on leaving the test interface are selected
automatically.Now, every candidate you invite to take the assessment will only be able to do so through the Smart
Browser. Candidates are prompted to download the Smart Browser from the link shared in the test invite mail.When the
candidate needs to click on the ‘start test’ button on the launch test screen, it opens in the Smart Browser. The
browser also prompts the candidate to switch to full-screen mode. Now, all candidates need to do is sign in and attempt
the test, as usual.
Also read: 6
Ways Candidates Try To Outsmart A Remote Proctored Assessment
2. Eagle-eyed online test monitoring leaves no room for error
Our AI-enabled Smart Browser takes frequent snapshots via the webcam, throughout the assessment.
Consequently, it is impossible to copy-paste code or impersonate a candidate.The browser prevents the following
candidate actions and facilitates thorough monitoring of the assessment:
Any candidate actions attempting to switch tabs with the intent to copy-paste or use a generative AI like ChatGPT
are shown a warning and captured in the candidate report.HackerEarth’s latest proctoring fixes
bulletproof our assessment platform, making it one of the most reliable and accurate sources of candidate hiring in the
market today.
Also read: 4 Ways
HackerEarth Flags The Use Of ChatGPT In Tech Hiring Assessments
Experience reliable assessments with the Smart Browser!
There you have it - our newest offering that preserves
the integrity of coding assessments and enables skill-first hiring, all in one go. Recruiters and hiring managers, this
is one feature that you can easily rely on and can be sure that every candidate’s test score is a result of their
ability alone.Curious to try out the Smart Browser? Well, don’t take our word for it. Head over here to check it out for
yourself!
We also love hearing from our customers so don’t hesitate to leave us any feedback you might
have.
In today’s fast-paced world, recruiting talent has become increasingly complicated. Technological advancements, high workforce expectations and a highly competitive market have pushed recruitment agencies to adopt innovative strategies for recruiting various types of talent. This article aims to explore one such recruitment strategy – headhunting.
What is Headhunting in recruitment?
In headhunting, companies or recruitment agencies identify, engage and hire highly skilled professionals to fill top positions in the respective companies. It is different from the traditional process in which candidates looking for job opportunities approach companies or recruitment agencies. In headhunting, executive headhunters, as recruiters are referred to, approach prospective candidates with the hiring company’s requirements and wait for them to respond. Executive headhunters generally look for passive candidates, those who work at crucial positions and are not on the lookout for new work opportunities. Besides, executive headhunters focus on filling critical, senior-level positions indispensable to companies. Depending on the nature of the operation, headhunting has three types. They are described later in this article. Before we move on to understand the types of headhunting, here is how the traditional recruitment process and headhunting are different.
How do headhunting and traditional recruitment differ from each other?
Headhunting is a type of recruitment process in which top-level managers and executives in similar positions are hired. Since these professionals are not on the lookout for jobs, headhunters have to thoroughly understand the hiring companies’ requirements and study the work profiles of potential candidates before creating a list.
In the traditional approach, there is a long list of candidates applying for jobs online and offline. Candidates approach recruiters for jobs. Apart from this primary difference, there are other factors that define the difference between these two schools of recruitment.
AspectHeadhuntingTraditional RecruitmentCandidate TypePrimarily passive candidateActive job seekersApproachFocused on specific high-level rolesBroader; includes various levelsScopeproactive outreachReactive: candidates applyCostGenerally more expensive due to expertise requiredTypically lower costsControlManaged by headhuntersManaged internally by HR teams
All the above parameters will help you to understand how headhunting differs from traditional recruitment methods, better.
Types of headhunting in recruitment
Direct headhunting: In direct recruitment, hiring teams reach out to potential candidates through personal communication. Companies conduct direct headhunting in-house, without outsourcing the process to hiring recruitment agencies. Very few businesses conduct this type of recruitment for top jobs as it involves extensive screening across networks outside the company’s expanse.
Indirect headhunting: This method involves recruiters getting in touch with their prospective candidates through indirect modes of communication such as email and phone calls. Indirect headhunting is less intrusive and allows candidates to respond at their convenience.Third-party recruitment: Companies approach external recruitment agencies or executive headhunters to recruit highly skilled professionals for top positions. This method often leverages the company’s extensive contact network and expertise in niche industries.
How does headhunting work?
Finding highly skilled professionals to fill critical positions can be tricky if there is no system for it. Expert executive headhunters employ recruitment software to conduct headhunting efficiently as it facilitates a seamless recruitment process for executive headhunters. Most software is AI-powered and expedites processes like candidate sourcing, interactions with prospective professionals and upkeep of communication history. This makes the process of executive search in recruitment a little bit easier. Apart from using software to recruit executives, here are the various stages of finding high-calibre executives through headhunting.
Identifying the role
Once there is a vacancy for a top job, one of the top executives like a CEO, director or the head of the company, reach out to the concerned personnel with their requirements. Depending on how large a company is, they may choose to headhunt with the help of an external recruiting agency or conduct it in-house. Generally, the task is assigned to external recruitment agencies specializing in headhunting. Executive headhunters possess a database of highly qualified professionals who work in crucial positions in some of the best companies. This makes them the top choice of conglomerates looking to hire some of the best talents in the industry.
Defining the job
Once an executive headhunter or a recruiting agency is finalized, companies conduct meetings to discuss the nature of the role, how the company works, the management hierarchy among other important aspects of the job. Headhunters are expected to understand these points thoroughly and establish a clear understanding of their expectations and goals.
Candidate identification and sourcing
Headhunters analyse and understand the requirements of their clients and begin creating a pool of suitable candidates from their database. The professionals are shortlisted after conducting extensive research of job profiles, number of years of industry experience, professional networks and online platforms.
Approaching candidates
Once the potential candidates have been identified and shortlisted, headhunters move on to get in touch with them discreetly through various communication channels. As such candidates are already working at top level positions at other companies, executive headhunters have to be low-key while doing so.
Assessment and Evaluation
In this next step, extensive screening and evaluation of candidates is conducted to determine their suitability for the advertised position.
Interviews and negotiations
Compensation is a major topic of discussion among recruiters and prospective candidates. A lot of deliberation and negotiation goes on between the hiring organization and the selected executives which is facilitated by the headhunters.
Finalizing the hire
Things come to a close once the suitable candidates accept the job offer. On accepting the offer letter, headhunters help finalize the hiring process to ensure a smooth transition.
The steps listed above form the blueprint for a typical headhunting process. Headhunting has been crucial in helping companies hire the right people for crucial positions that come with great responsibility. However, all systems have a set of challenges no matter how perfect their working algorithm is. Here are a few challenges that talent acquisition agencies face while headhunting.
Common challenges in headhunting
Despite its advantages, headhunting also presents certain challenges:
Cost Implications: Engaging headhunters can be more expensive than traditional recruitment methods due to their specialized skills and services.
Time-Consuming Process: While headhunting can be efficient, finding the right candidate for senior positions may still take time due to thorough evaluation processes.
Market Competition: The competition for top talent is fierce; organizations must present compelling offers to attract passive candidates away from their current roles.
Although the above mentioned factors can pose challenges in the headhunting process, there are more upsides than there are downsides to it. Here is how headhunting has helped revolutionize the recruitment of high-profile candidates.
Advantages of Headhunting
Headhunting offers several advantages over traditional recruitment methods:
Access to Passive Candidates: By targeting individuals who are not actively seeking new employment, organisations can access a broader pool of highly skilled professionals.
Confidentiality: The discreet nature of headhunting protects both candidates’ current employment situations and the hiring organisation’s strategic interests.
Customized Search: Headhunters tailor their search based on the specific needs of the organization, ensuring a better fit between candidates and company culture.
Industry Expertise: Many headhunters specialise in particular sectors, providing valuable insights into market dynamics and candidate qualifications.
Conclusion
Although headhunting can be costly and time-consuming, it is one of the most effective ways of finding good candidates for top jobs. Executive headhunters face several challenges maintaining the g discreetness while getting in touch with prospective clients. As organizations navigate increasingly competitive markets, understanding the nuances of headhunting becomes vital for effective recruitment strategies. To keep up with the technological advancements, it is better to optimise your hiring process by employing online recruitment software like HackerEarth, which enables companies to conduct multiple interviews and evaluation tests online, thus improving candidate experience. By collaborating with skilled headhunters who possess industry expertise and insights into market trends, companies can enhance their chances of securing high-caliber professionals who drive success in their respective fields.
The job industry is not the same as it was 30 years ago. Progresses in AI and automation have created a new work culture that demands highly skilled professionals who drive innovation and work efficiently. This has led to an increase in the number of companies reaching out to external sources of recruitment for hiring talent. Over the years, we have seen several job aggregators optimise their algorithms to suit the rising demand for talent in the market and new players entering the talent acquisition industry. This article will tell you all about how external sources of recruitment help companies scout some of the best candidates in the industry, the importance of external recruitment in organizations across the globe and how it can be leveraged to find talent effectively.
Understanding external sources of recruitment
External sources refer to recruitment agencies, online job portals, job fairs, professional associations and any other organizations that facilitate seamless recruitment. When companies employ external recruitment sources, they access a wider pool of talent which helps them find the right candidates much faster than hiring people in-house. They save both time and effort in the recruitment process.
Online job portals
Online resume aggregators like LinkedIn, Naukri, Indeed, Shine, etc. contain a large database of prospective candidates. With the advent of AI, online external sources of recruitment have optimised their algorithms to show the right jobs to the right candidates. Once companies figure out how to utilise job portals for recruitment, they can expedite their hiring process efficiently.
Social Media
Ours is a generation that thrives on social media. To boost my IG presence, I have explored various strategies, from getting paid Instagram users to optimizing post timing and engaging with my audience consistently. Platforms like FB an IG have been optimized to serve job seekers and recruiters alike. The algorithms of social media platforms like Facebook and Instagram have been optimised to serve job seekers and recruiters alike. Leveraging them to post well-placed ads for job listings is another way to implement external sources of recruitment strategies.
Employee Referrals
Referrals are another great external source of recruitment for hiring teams. Encouraging employees to refer their friends and acquaintances for vacancies enables companies to access highly skilled candidates faster.
Campus Recruitment
Hiring freshers from campus allows companies to train and harness new talent. Campus recruitment drives are a great external recruitment resource where hiring managers can expedite the hiring process by conducting screening processes in short periods.
Recruitment Agencies
Companies who are looking to fill specific positions with highly skilled and experienced candidates approach external recruitment agencies or executive headhunters to do so. These agencies are well-equipped to look for suitable candidates and they also undertake the task of identifying, screening and recruiting such people.
Job Fairs
This is a win-win situation for job seekers and hiring teams. Job fairs allow potential candidates to understand how specific companies work while allowing hiring managers to scout for potential candidates and proceed with the hiring process if possible.
Importance of External Recruitment
The role of recruitment agencies in talent acquisition is of paramount importance. They possess the necessary resources to help companies find the right candidates and facilitate a seamless hiring process through their internal system. Here is how external sources of recruitment benefit companies.
Diversity of Skill Sets
External recruitment resources are a great way for companies to hire candidates with diverse professional backgrounds. They possess industry-relevant skills which can be put to good use in this highly competitive market.
Fresh Perspectives
Candidates hired through external recruitment resources come from varied backgrounds. This helps them drive innovation and run things a little differently, thus bringing in a fresh approach to any project they undertake.
Access to Specialized Talent
Companies cannot hire anyone to fill critical roles that require highly qualified executives. This task is assigned to executive headhunters who specialize in identifying and screening high-calibre candidates with the right amount of industry experience. Huge conglomerates and companies seek special talent through external recruiters who have carved a niche for themselves.
Now that you have learnt the different ways in which leveraging external sources of recruitment benefits companies, let’s take a look at some of the best practices of external recruitment to understand how to effectively use their resources.
Best Practices for Effective External Recruitment
Identifying, reaching out to and screening the right candidates requires a robust working system. Every system works efficiently if a few best practices are implemented. For example, hiring through social media platforms requires companies to provide details about their working environment, how the job is relevant to their audience and well-positioned advertisements. The same applies to the other external sources of recruitment. Here is how you can optimise the system to ensure an effective recruitment process.
Craft Clear and Compelling Job Descriptions
Detail Responsibilities: Clearly outline the key responsibilities and expectations for the role.
Highlight Company Culture: Include information about the company’s mission, values, and growth opportunities to attract candidates who align with your organizational culture.
Leverage Multiple Recruitment Channels
Diversify Sources: Use a mix of job boards, social media platforms, recruitment agencies, and networking events to maximize reach. Relying on a single source can limit your candidate pool.
Utilize Industry-Specific Platforms: In addition to general job boards, consider niche job sites that cater to specific industries or skill sets
Streamline the Application Process
Simplify Applications: Ensure that the application process is user-friendly. Lengthy or complicated forms can deter potential candidates from applying.
Mobile Optimization: Many candidates use mobile devices to apply for jobs, so ensure your application process is mobile-friendly.
Engage in Proactive Sourcing
Reach Out to Passive Candidates: Actively seek out candidates who may not be actively looking for a job but could be a great fit for your organization. Use LinkedIn and other professional networks for this purpose.
Maintain a Talent Pool: Keep a database of previous applicants and strong candidates for future openings, allowing you to reach out when new roles become available.
Utilize Social Media Effectively
Promote Job Openings: Use social media platforms like LinkedIn, Facebook, and Twitter to share job postings and engage with potential candidates. This approach can also enhance your employer brand
Conduct Background Checks: There are several ways of learning about potential candidates. Checking out candidate profiles on job boards like LinkedIn or social media platforms can give companies a better understanding of their potential candidates, thus confirming whether they are the right fit for the organization.
Implement Data-Driven Recruitment
Analyze Recruitment Metrics: Track key metrics such as time-to-hire, cost-per-hire, and source effectiveness. This data can help refine your recruitment strategies over time. Using external hiring software like HackeEarth can streamline the recruitment process, thus ensuring quality hires without having to indulge internal resources for the same.
Use Predictive Analytics: In this age of fast paced internet, everybody makes data-driven decisions. Using predictive analytics to study employee data will help companies predict future trends, thus facilitating a productive hiring process.
Conclusion
External sources of recruitment play a very important role in an organization’s talent acquisition strategy. By employing various channels of recruitment such as social media, employee referrals and campus recruitment drives, companies can effectively carry out their hiring processes. AI-based recruitment management systems also help in the process. Implementing best practices in external recruitment will enable organizations to enhance their hiring processes effectively while meeting their strategic goals.
The Progressive Pre-Employment Assessment is a crucial step in the hiring process, as it evaluates candidates through various dimensions including cognitive abilities, personality traits, and role-specific skills.
While employers and recruiters have this in the palm of their hand, candidates who master it will successfully navigate the assessment and have a higher chance of landing that dream job. But what does it entail in the first place?
Candidates can expect to undergo tests that assess verbal, numerical, and work style capabilities, as well as a personality assessment. Hence, understanding the structure and purpose of the Progressive Pre-Employment Assessment can give candidates a competitive edge. But before one tackles online tests, we must first dissect what this assessment is and what it consists of.
The evolution of pre-employment assessments
Pre-employment assessments have undergone significant changes over the decades, from rudimentary tests to sophisticated, modern evaluations. Let’s put the two side by side.
Traditional methods:
Initially, pre-employment assessments focused on basic skills and educational qualifications. These paper-based tests primarily assessed cognitive and verbal abilities, without any conclusions about the candidates’ output in very specific situations.
Modern techniques:
Today, online assessments are prevalent, evaluating a variety of dimensions, including cognitive skills, personality traits, and behavioral evaluations. These tools offer a more comprehensive view of a candidate's job performance potential, while, at the same time, saving precious time for both parties involved.
In today’s competitive job market, progressive pre-employment assessments play a crucial as they not only measure technical skills and knowledge but also provide insights into a candidate's ethical bias, cultural fit, and communication skills.
Likewise, assessment tests have evolved to include situational judgment tests and culture fit analyses, which are pivotal in assessing the suitability of a candidate for specific roles. And this isn’t just in terms of skillsets—they help in identifying candidates who align well with the company's values and working environment.
This is mainly for the tests’ ability to accurately gauge a candidate's interpersonal skills and emotional intelligence, which are essential for roles that require teamwork and client interactions.
What are progressive pre-employment assessments?
Progressive pre-employment assessments are structured evaluations designed to judge a candidate’s abilities and fit for a role at Progressive Insurance. Unlike traditional aptitude tests, these assessments encompass various elements such as cognitive abilities, situational judgments, and personality traits.
These tests typically include verbal and numerical reasoning sections, as well as work style assessments that gauge behavioral tendencies. Through this merger of multiple dimensions, Progressive seeks to understand not just the skills and knowledge of the candidate, but also their ethical perspectives and communication skills.
Components of a progressive assessment strategy
What sets progressive assessments apart? Well, as most employers just focus on the basic credentials and competencies, the comprehensive assessment strategy at Progressive includes several key components:
Cognitive evaluations: These tests measure candidates' logical reasoning and problem-solving capabilities through verbal, numerical, and abstract reasoning questions.
Personality assessments: These tests evaluate traits and tendencies to understand how a candidate might behave in various workplace scenarios. They aim to provide insight into their ethical bias and interpersonal skills.
Behavioral evaluations: These sections analyze how candidates might act in specific situations, ensuring a good cultural fit and alignment with Progressive's values.
Role-specific skills tests: These assessments focus on the specialized skills required for the position, ensuring the candidate has the necessary technical knowledge and expertise.
Implementing progressive assessments
Successful implementation of Progressive Assessments in the hiring process requires designing an effective assessment process and following best practices for administration. This ensures accuracy, better data security, and reliable decision-making. In particular, the implementation hinges on the feasibility of the original design.
Step 1 --- Designing the assessment process
Designing an effective Progressive Assessment involves understanding the specific needs of the role and the company's approach to hiring. Each test component — verbal, numerical, and work style — must align with the desired skills and personality traits for the role.
HR teams need to define clear objectives for each assessment section. This includes establishing what each part aims to evaluate, like the problem-solving or personality assessments. Incorporating legal and policy guidelines ensures the assessments are fair and non-discriminatory, which is crucial for avoiding legal issues.
Likewise, everaging online assessment tests provides flexibility and efficiency. These tests allow candidates to complete them remotely, easing logistics and scheduling concerns. Ensuring security is also essential, and implementing testing and other recruitment tools can help enhance data security and accuracy.
Step 2 --- Best practices for assessment administration
Administering assessments effectively revolves around consistency and fairness. Establish structured guidelines for the administration process to ensure each candidate undergoes the same conditions, promoting reliability. This includes standardizing the timing, environment, and instructions for all assessments.
Training HR representatives is vital. They should be well-versed in handling the assessments, from initial candidate interactions to evaluating the results. Regular training updates ensure the team remains knowledgeable about best practices and any new tools used in the assessment process.
Administering assessments also involves maintaining better data security and accuracy. This is achieved by utilizing secure online platforms and ensuring that only authorized personnel have access to sensitive data. Leveraging top API penetration testing tools is one approach to securing candidate data and preserving the integrity of the assessment process.
Implementing consistent feedback mechanisms for candidates can also improve the process. Providing insights on their performance helps candidates understand their strengths and areas for growth, which reflects positively on the company’s commitment to candidate experience.
Benefits of progressive assessments
Progressive assessments offer significant advantages in the hiring process, such as improving the accuracy of hiring decisions and enhancing the overall candidate experience. These benefits help companies find better-fitting candidates and reduce turnover rates.
1. Improved hiring accuracy
Progressive pre-employment assessments allow companies to evaluate candidates more comprehensively. By assessing personality traits, cognitive abilities, and ethical biases, employers can identify individuals who align with the company’s values and have the necessary skills for the job.
For example, personality assessments can pinpoint traits like empathy, communication, and problem-solving abilities. This helps employers select candidates who are not only qualified but also fit well within the team. Evaluating these qualities ensures that new hires can thrive in customer service roles where empathy and effective communication are crucial.
Moreover, using tools like the DDI Adaptive Reasoning Test helps to simulate real job tasks. This gives employers deeper insights into a candidate's capability to handle job-specific challenges. As a result, the company is more likely to experience lower turnover rates due to better candidate-job fit.
2. Enhanced candidate experience
A well-structured assessment process can significantly enhance the candidate experience. Clear instructions,fair testing procedures, and timely feedback create a positive impression of the company. Candidates appreciate transparency and feel valued when the process is designed with their experience in mind.
Implementing assessments that reflect actual job roles and responsibilities gives candidates a realistic preview of the job. This reduces later dissatisfaction and turnover. Additionally, personality assessments that highlight traits such as confidence and empathy provide a more engaging candidate experience.
Companies can also strengthen their employer brand by showcasing their commitment to a fair and comprehensive hiring process. Providing resources like practice tests helps candidates feel better prepared and less anxious about the assessment, leading to a more positive perception of the company.
Common pitfalls in progressive assessments
Candidates often struggle with the cognitive abilities section, which requires strong analytical skills and problem-solving capabilities. The situational judgment tests can also be tricky as they assess empathy, decision-making, and customer service scenarios. Personality assessments can pose challenges as well, especially for those unsure how to present their personality traits aligned with the job role.
A significant issue is also misinterpretation of the test's format and expectations. Many find it daunting to navigate through various sections, such as verbal, numerical, and work style assessments. Lastly, some candidates might overlook the legal nuances of personality assessments or document redaction protocols, leading to compliance issues.
Strategies to overcome challenges
To tackle cognitive abilities assessments, candidates should engage in consistent practice with sample questions and mock tests. This helps enhance their analytical and problem-solving skills. For situational judgment tests, it is essential to practice empathy and customer service scenarios to develop a better understanding of role-specific challenges.
In personality assessments, being honest while demonstrating relevant personality traits like being a team player is crucial. Seeking guidance from study materials such as Job Test Prep can provide a realistic testing environment.
Understanding legal considerations, such as those around document redaction, is important for compliance. Utilizing a document redaction SDK can ensure adherence to required policies. Familiarity with each section's format will aid in navigating the assessments confidently and effectively.
Trends and innovations in employee assessments
There is a growing emphasis on AI-powered assessments —these tools analyze vast amounts of data to predict a candidate's job performance, ensuring a more objective and efficient selection process.
Personality assessments are evolving to include metrics like empathy and communication skills, which are crucial for roles in customer service and other people-centric positions.
Additionally, gamified assessments, which make the evaluation process engaging, are gaining popularity. They not only assess problem-solving skills but also gauge how candidates perform under pressure.
Organizations can prepare for the future by integrating cutting-edge technologies into their hiring processes. Investing in training for evaluators to accurately interpret new assessment metrics is crucial. This involves
Moreover, companies should stay updated on legal requirements to maintain compliance and ensure fair assessment practices.
Encouraging candidates to focus on developing their personality traits, such as being team players and showing confidence, can also better prepare them for progressive assessments that look beyond technical skills.
The strategic value of progressive assessments
Progressive pre-employment assessments rigorously evaluate candidates on multiple fronts, including cognitive abilities, situational judgment, personality fit, and role-specific skills. This multifaceted approach not only helps in identifying the best match for specific roles but also reduces the risk of bad hires.
By investing in these assessments, companies can significantly enhance their recruitment processes. Consistent use of these tools leads to more informed decision-making, reducing turnover rates and ensuring employee retention.
Appropriate preparation and implementation of these assessments can streamline the hiring pipeline, saving time and resources. Furthermore, this approach bolsters team performance and aligns employee roles with their strengths, promoting a culture of efficiency and productivity. While Progressive is far from the only company using this approach, they’ve set a standard in terms of looking at candidates holistically and making sure they’re truly ready for the job.
Frequently Asked Questions
This section covers common inquiries related to the Progressive Pre-Employment Assessments, including differences from psychometric tests, benefits for small businesses, legal considerations, and the role of technology.
How do progressive assessments differ from psychometric testing?
Progressive assessments typically examine a candidate's ethical bias and personality traits. In contrast, psychometric tests focus on cognitive abilities and personality dimensions. The Progressive Pre-Employment Assessment includes verbal, numerical, and work style components, offering a broader evaluation spectrum.
Can small businesses benefit from implementing progressive assessment strategies?
Small businesses can gain significant advantages from adopting progressive assessment strategies. These assessments help identify candidates that align closely with the company’s values and culture, reducing turnover rates. Additionally, they provide insights into a candidate's ethical stance and work style, which are crucial for cohesive team dynamics.
What are the legal considerations when using pre-employment assessments?
Legal considerations include ensuring compliance with equal employment opportunity laws and avoiding discrimination based on race, gender, or disability. It is essential to validate the assessment tools and ensure they are scientifically proven to be fair. Companies must also maintain transparency about the purpose and usage of the assessments.
How can technology enhance the effectiveness of progressive assessments?
Technology can streamline the assessment process by allowing candidates to complete the tests remotely. Advanced analytics help in the accurate interpretation of results, ensuring a better match between the candidate and the job role. Many platforms offer practice tests that mirror the actual assessment, aiding in preparation and reducing test anxiety.
Our AI-enabled Smart Browser takes frequent snapshots via the webcam, throughout the assessment.
Consequently, it is impossible to copy-paste code or impersonate a candidate.The browser prevents the following
candidate actions and facilitates thorough monitoring of the assessment:
