Home
/
Blog
/
AI Recruiting
/
How can mathematics make you a better recruiter!

How can mathematics make you a better recruiter!

Author
Rashmi Jain
Calendar Icon
October 6, 2017
Timer Icon
3 min read
Share
Hiring the right talent is crucial to any organization’s growth and success. “By implementing recruiting best practices and supporting technology, you can potentially reduce your time to hire by up to 50 percent, reduce cost per hire by up to 70 percent, and improve recruiter efficiency while finding the talent you need for driving business results.”1

Companies try everything from recruiting agencies to job boards to employee referrals to social media. But the efficacy of these approaches can be often debatable. But that’s a post for another day.

Here, we bring you some sure-fire concepts that will boost hiring efficiency.

Did you say mathematics?

Yes, you read it right. Mathematics! Perhaps the most hated subject ever, math can help recruiters solve one of the most prominent problems they face while trying to zero in on great talent.

Let’s see how exactly it helps.

How many candidates should you interview before making a decision to hire? Imagine a situation where you have a hundred applicants for a position. The problem is that neither will you interview just one candidate nor will you interview all hundred. The dilemma is not whom to pick but how many to even consider before you hire (or you give up).

The most intuitive answer would be that it requires a balance between looking and leaping - that you must look at enough candidates to build a standard and decide on whatever satisfies the established standard. This looks like the perfect answer but here is the catch. Most people can’t say what this standard or balance should be. Luckily, mathematics comes to your rescue and provides the answer. Optimal Stopping Theory...

It is an idea “that every decision is a decision to stop what you are going to make a decision.”2 The theory suggests that you should reject the initial 37% of all the applicants and hire only after that. After this point, you should select the next applicant who is better than all candidates you interviewed before the cutoff. This is not intuition or a compromise between looking and leaping. It is a probable result.



Here look at this example if you have five weeks to choose a primary contractor. You could expect to see possibly four a week; that is an anticipated total of 20 suppliers. If you selected normally and selected the first ‘good enough’ option, the probability of finding the optimum supplier is just 5%. However, if you rejected the first 37% suppliers, in this case, 18 suppliers, and then selected the next supplier that was better than all the previous suppliers, then your odds of selecting the optimum supplier would increase to 40% (For the more curious people, go here to read about the famous example, the Secretary problem.)

This is just one of the mathematical theories that can help recruiters. To list a few more, there is Negativity Threshold which can help you identify the candidates that are inconsistent in their interview answers or are withholding information. Negativity Threshold was presented by Hannah Fry in her TED talk “The Mathematics of Love.” It was coined by John Gottman by observing how couples interact with each other.

The equations look something like this:

Mathematics, Recruiter, Mathematics in hiring, mathematics example, Recruitment, Hiring, Mathematics in recruitment

The left-hand side of the equation tells how positive or negative a wife/husband will be in the next thing she/he says. Here, w is the mood of the wife in general, r_w.W_t is the mood of the wife when she’s with her husband and I_(HM) is the influence that her husband’s actions will have on her. Researchers have plotted the effects the two partners have on each other. The plot looks as follows:

Mathematics, Recruiter, Mathematics in hiring, mathematics example, Recruitment, Hiring, Mathematics in recruitment

Here, the term T_ is the negativity threshold. At this point, the husband’s negative impact becomes so high that the wife responds with more negativity. To know more about this theory you can watch [ted talk link] or read this.

The negativity threshold suggests you be upfront about any issues and get all sorts of concerns out in open to avoid issues further down the line.

Another interesting equation that is worth looking at is The Drake Equation. The equation was conceived in 1961 by Dr. Frank Drake in an attempt to find the number of potential extraterrestrial bodies with life in the universe. He took something extremely complex and daunting and broke it down into something easy to understand. The Drake equation looks something like this:
N = R*•fp• ne• fl• fi• fc• L

The equation involves various factors such as the average rate of star formation in our galaxy, the fraction of stars that have formed planets, and much more which we will not get into. But, what how does this apply to hiring practices? A very obvious similarity is that both use data to pinpoint something or “someone” out there.

An important step in hiring candidates is determining the business factors your company wants to improve, says Emilio J. Castilla, Nanyang Technological University professor of management at the MIT Sloan School of Management. Determining these factors brings clarity to the business and helps everyone understand their roles. For instance, there is something called Sales Velocity, defined as:
Sales Velocity = Work In Progress ? Win Rate ? Avg. Deal Size ÷ Time Taken To Close

This equation does not help in identifying the top performers but also helps in determining the areas where an individual needs to improve. Drake’s theory is extremely useful when it comes to bringing order to a chaotic world.

Although most of the recruiting process is often dominated by emotion, mathematics is the one subject which can be applied everywhere, even hiring, without this particular bias.

Can you math enthusiasts think of any more? Let us know in Comments.

& for some of you who are super busy or are less inclined to "appreciating" math concepts, let us do the work for you.

Take a free trial for our Online Assessment software to hire the best mathematician (or developers) in your talent pipeline

Subscribe to The HackerEarth Blog

Get expert tips, hacks, and how-tos from the world of tech recruiting to stay on top of your hiring!

Author
Rashmi Jain
Calendar Icon
October 6, 2017
Timer Icon
3 min read
Share

Hire top tech talent with our recruitment platform

Access Free Demo
Related reads

Discover more articles

Gain insights to optimize your developer recruitment process.

The Mobile Dev Hiring Landscape Just Changed

Revolutionizing Mobile Talent Hiring: The HackerEarth Advantage

The demand for mobile applications is exploding, but finding and verifying developers with proven, real-world skills is more difficult than ever. Traditional assessment methods often fall short, failing to replicate the complexities of modern mobile development.

Introducing a New Era in Mobile Assessment

At HackerEarth, we're closing this critical gap with two groundbreaking features, seamlessly integrated into our Full Stack IDE:

Article content

Now, assess mobile developers in their true native environment. Our enhanced Full Stack questions now offer full support for both Java and Kotlin, the core languages powering the Android ecosystem. This allows you to evaluate candidates on authentic, real-world app development skills, moving beyond theoretical knowledge to practical application.

Article content

Say goodbye to setup drama and tool-switching. Candidates can now build, test, and debug Android and React Native applications directly within the browser-based IDE. This seamless, in-browser experience provides a true-to-life evaluation, saving valuable time for both candidates and your hiring team.

Assess the Skills That Truly Matter

With native Android support, your assessments can now delve into a candidate's ability to write clean, efficient, and functional code in the languages professional developers use daily. Kotlin's rapid adoption makes proficiency in it a key indicator of a forward-thinking candidate ready for modern mobile development.

Breakup of Mobile development skills ~95% of mobile app dev happens through Java and Kotlin
This chart illustrates the importance of assessing proficiency in both modern (Kotlin) and established (Java) codebases.

Streamlining Your Assessment Workflow

The integrated mobile emulator fundamentally transforms the assessment process. By eliminating the friction of fragmented toolchains and complex local setups, we enable a faster, more effective evaluation and a superior candidate experience.

Old Fragmented Way vs. The New, Integrated Way
Visualize the stark difference: Our streamlined workflow removes technical hurdles, allowing candidates to focus purely on demonstrating their coding and problem-solving abilities.

Quantifiable Impact on Hiring Success

A seamless and authentic assessment environment isn't just a convenience, it's a powerful catalyst for efficiency and better hiring outcomes. By removing technical barriers, candidates can focus entirely on demonstrating their skills, leading to faster submissions and higher-quality signals for your recruiters and hiring managers.

A Better Experience for Everyone

Our new features are meticulously designed to benefit the entire hiring ecosystem:

For Recruiters & Hiring Managers:

  • Accurately assess real-world development skills.
  • Gain deeper insights into candidate proficiency.
  • Hire with greater confidence and speed.
  • Reduce candidate drop-off from technical friction.

For Candidates:

  • Enjoy a seamless, efficient assessment experience.
  • No need to switch between different tools or manage complex setups.
  • Focus purely on showcasing skills, not environment configurations.
  • Work in a powerful, professional-grade IDE.

Unlock a New Era of Mobile Talent Assessment

Stop guessing and start hiring the best mobile developers with confidence. Explore how HackerEarth can transform your tech recruiting.

Vibe Coding: Shaping the Future of Software

A New Era of Code

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.

Guide to Conducting Successful System Design Interviews in 2025

What is Systems Design?

Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.

Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.

Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.

What is a System Design Interview?

Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:

How they navigate technical complexity and navigate uncertainty
How they meet expectations of scale, security and speed
How they focus on the bigger picture without losing sight of details

This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.

What are some common topics for a System Design Interview

System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:

How would you approach the design of a social media app or video app?

What are some ways to design a search engine or a ticketing system?

How would you design an API for a payment gateway?

What are some trade-offs and constraints you will consider while designing systems?

What is your rationale for taking a particular approach to problem solving?

Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.

For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.

The Difference between a System Design Interview and a Coding Interview

If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.

Here are three key difference between the two:

Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.

Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.

Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.

How to Conduct an Effective System Design Interview

One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview.
Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:

Step 1: Understand the subject at hand

  • Develop an understanding of basics of system design and architecture
  • Familiarize yourself with commonly asked systems design interview questions
  • Read about system design case studies for popular applications
  • Structure the questions and problems by increasing magnitude of difficulty

Step 2: Prepare for the interview

  • Plan the extent of the topics and scope of discussion in advance
  • Clearly define the evaluation criteria and communicate expectations
  • Quantify constraints, inputs, boundaries and assumptions
  • Establish the broader context and a detailed scope of the exercise

Step 3: Stay actively involved

  • Ask follow-up questions to challenge a solution
  • Probe candidates to gauge real-time logical reasoning skills
  • Make it a conversation and take notes of important pointers and outcomes
  • Guide candidates with hints and suggestions to steer them in the right direction

Step 4: Be a collaborator

  • Encourage candidates to explore and consider alternative solutions
  • Work with the candidate to drill the problem into smaller tasks
  • Provide context and supporting details to help candidates stay on track
  • Ask follow-up questions to learn about the candidate’s experience

Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.

Evaluation Rubric for Candidates

Facilitate Successful System Design Interview Experiences with FaceCode

FaceCode, HackerEarth’s intuitive and secure platform, empowers recruiters to conduct system design interviews in a live coding environment with HD video chat.

FaceCode comes with an interactive diagram board which makes it easier for interviewers to assess the design thinking skills and conduct communication assessments using a built-in library of diagram based questions.

With FaceCode, you can combine your feedback points with AI-powered insights to generate accurate, data-driven assessment reports in a breeze. Plus, you can access interview recordings and transcripts anytime to recall and trace back the interview experience.

Learn how FaceCode can help you conduct system design interviews and boost your hiring efficiency.

Top Products

Explore HackerEarth’s top products for Hiring & Innovation

Discover powerful tools designed to streamline hiring, assess talent efficiently, and run seamless hackathons. Explore HackerEarth’s top products that help businesses innovate and grow.
Frame
Hackathons
Engage global developers through innovation
Arrow
Frame 2
Assessments
AI-driven advanced coding assessments
Arrow
Frame 3
FaceCode
Real-time code editor for effective coding interviews
Arrow
Frame 4
L & D
Tailored learning paths for continuous assessments
Arrow
Get A Free Demo