Hiring and talent acquisition are the cornerstones of business growth. When you need to scale your business, you look at the recruiting teams to bring in the talent needed for success. Hiring at scale is not an easy feat, and doing it well without having an analytical and data-driven recruiting approach is even harder.

Why is data important in tech recruiting? Let’s break this down logically. When you hire in large numbers – say thousands of tech hires in a year; you want to be as efficient as possible. To do so you need to know which channels are working better than others. Are most of your hires responding to your LinkedIn ad, or is GitHub the platform of choice for new hires? Conversely, are the channels different when it comes to hiring interns versus lateral hires?

What is data-driven recruiting?

TTH (Time To Hire) is a metric every recruiter is familiar with. Ideally, recruiters like to keep their TTH low. You cannot, however, do this if you’re not aware of what works and what doesn’t. This is possible only when you have looked at the hiring data and found patterns that work, and those that don’t. Data-driven recruiting makes this possible.

In the simplest of terms, data-driven recruiting is a scientific method of collecting, analyzing, and using analytical data about candidate behavior to make inferences that are used to drive decisions throughout the tech hiring funnel.

What are the benefits of data-driven recruitment?

We know that tech recruiting is a multi-dimensional process. There are a number of elements that affect every stage of the recruitment funnel. Being aware of the right metrics enables tech recruiters in streamlining and optimizing every step of the funnel to increase overall effectiveness.

Also Read: How To Get Your Recruiting Metrics Right In 2022

There is a singular goal to this process: to hire better and get the best possible ROI for the time that a recruiter spends trying to fill a vacant role. In many ways, data-driven recruitment empowers recruiters to make educated opinions and change their hiring strategy (if needed) through the long-winding process of developer recruitment.

Data driven recruiting insights | HackerEarth

What kind of data should I be tracking?

One of the most important aspects of using data for decision making is to know which data to look at, and which is irrelevant. Let’s take a look at some of the key recruitment metrics related to tech hiring that every recruiter needs to keep an eye on.

These metrics would provide a good launch platform to optimize your recruiting and onboarding process with available data:

1. Cost To Hire (CTH)

The end result of hiring is onboarding a developer with a definite CTC. That, however, is not the only expense involved in hiring said developer.

The CTH of hiring a developer can be split into two halves:

a. Internal recruiting costs: This involves any and every internal expense including (but not limited to) employee referral incentives, recruiters’ salaries, and interviewing costs. You can calculate interviewing costs by the following formula:

Interviewing Cost = Number of hours of interviews X Hourly salary of involved employees

Since tech recruiting can involve interviews with engineering managers and CTOs, hence the interviewing cost for every developer would take into account all shareholders across the process.

b. External recruiting costs: This includes expenses incurred as part of banding and marketing costs, recruitment software and events, and external recruiter agency fees.

Your final CTH or cost per hire would then be calculated as:

CPH = Total internal cost + Total external cost / Total number of hires

2. Time To Fill (TTF) and Time To Hire (TTH)

While both these terms sound similar, the difference is very important for recruiters.

‘Time To Fill’ refers to the time taken to fill a position from the moment the position was advertised, until a candidate accepts the job offer, and the position is filled.

‘Time To Hire’ on the other hand only estimates the time it takes from first contact (i.e. the first phone call or meeting) until the job offer is accepted.

If a position is taking longer to fill, then you must take a look at the strategy for advertising and outreach. Is the job position easily noticeable and searchable on the website? Has there been enough efforts on the social handles to promote the role?

However, if your TTH is on the higher side, then you have to consider if your interviews are longer than needed. Are you spending too much time on assignments, or are there any other stages of the hiring process that you can cut down? Sometimes, a lot of time goes by in trying to get all stakeholders on the same page, and getting feedback post-interview. If these are the steps that are inflating your TTH, then you should have a talk with all involved team members.

3. Candidate Experience Metrics

In recent years, the term candidate experience has gained notoriety in tech hiring circles. It refers to candidates’ overall impression of your company’s recruitment processes. This takes into account all the various touch points right from the moment a candidate browses your careers page, the emails and other communiques sent out to them, the process of assessments and interviews, up until they receive a job offer or rejection email (or are ghosted in some cases).

At every step of the way, candidates are forming an opinion not just about your company, but also about how you treat a prospective employee. Many developers choose to share their opinions on sites like Glassdoor or with their friends and colleagues, and these reviews and word-of-mouth opinions can impact your reputation as an employer.

Candidate experience survey sample | HackerEarth

In order to understand what candidates think about your brand, get the data from the horses’ mouth (figuratively speaking!). Hiring a third-party research company to create anonymous, objective measurements and surveys is a great idea. Alternatively, you can create a candidate experience survey yourself, and send it to a large pool of candidates and new hires. Remember to include candidates that have rejected your offer, or dropped off after the initial chat. The more diverse the sample pool, the better your insights.

4. Quality Of Hire (QoH)

Quality is indeed a subjective metric, but there are ways in which you can compare the quality of a current hire with past hires. Look at the value the new hire is adding to the organization i.e. the new hire’s performance as compared to pre-hire expectations. The QoH of any hire should be determined within the first year of their joining the organization. Doing so helps you understand the outcomes delivered by your current recruitment practices.

Sometimes, a candidate can check all the right boxes during assessments and interviews, only to find that they are not up to the daily work routine. Research says that as many as 1 in 4 new hires will quit a job in their first six months. If this is an issue you are grappling with, then it’s time to question the quality of your hires and find out ways to improve your QoH.

There is no exact formula to define QoH, but some recruiters like to define it as:

QoH = (Indicator A% + Indicator B% + Indicator C%…) ÷ Number of Indicators

This formula uses agreed upon indicators of performance to calculate QoH. For a tech hire, these indicators can be the number of projects they complete in a month, or their code quality.

Another way to calculate QoH is by using the Net Hiring Score. This is a scale of 0-10 (with 0 being poor, and 10 being excellent), which managers can use to rate a new hire. The employee is also given a similar scorecard which they can use to rate job fit and whether the company meets their expectations.

Your Net Hiring Score is therefore defined as:

Net Hiring Score = Percentage of poor fits (0-6) – Percentage of great fits (scaled 9 or 10) X 100

If the result is <0, too many poor fits are being hired, but a number greater than 0 indicates more great fits are being hired, which is what recruiters should be aiming for.

5. Diversity and inclusion metrics

For a long time, diversity was limited to having an equal ratio of men and women in the workplace. Today, the definition of diversity extends beyond gender to include race, nationality, education level, age, disability, family status, employment status (full-time, part-time, flexible), immigration status, and much more.

Monitoring these metrics should be contextual to an organization’s local milieu. Recruiters should look at the issues being highlighted by the tech community in their area and try to address those. Every nation has different legal, political, historical, and cultural environments which determine relevant diversity metrics. While gender inequality is a global issue, some locations may have an additional religious or ethnical bias, which you would need to correct.

While we agree that developing a multicultural organization with all-inclusive policies can be challenging, this is where data analytics can play a huge role in creating awareness. By identifying patterns of behavior and bias, we can highlight the areas where a company, or an individual who’s also a decision maker, is being exclusive or prejudiced. Identifying these voids is the first step to adapting and developing diversity in recruitment. You can then use these insights to create a process that sidesteps these challenges and promotes equity and equality.

How to implement a data-driven recruiting process?

There is an apt idiom in the tech world -Data in, Data out. To fuel a data-driven hiring process, you need to first ensure you are collecting data efficiently. Choose the metrics you want to measure, and create a streamlined methods of collecting these data points.

A data-driven recruiting strategy can be designed using the following steps:

Create Applicant Funnels
Evaluate At Scale
Improve Close Rate
Post-Hiring Evaluations

At HackerEarth, we like to use the following funnel:

Engage > Source > Assess > Interview > Onboard > Upskill

This allows us to have a bird’s eye view of the entire hiring and retention funnel, while being able to break it down into segments and measure each effectively. For instance, if the Source > Assess segment is showing a huge time lag, then we know that we have to increase the speed at which we create and send assessments to candidates. Or if the Assess > Interview segment is what is slowing us down, then we can improve on how we gather feedback and action upon it, and connect with the hiring managers to ensure their availability for interviews.

Whether you are evaluating thousands of developers for a role, or talking to passive candidates for a lateral role, the larger your data set and the more detailed your report, the stronger your process will be. Keep details of every candidate interaction and action. How long did it take candidates to submit a coding assessment? How long for feedback, or interviews? Having these metrics on paper will help you point out the gaps in your process and improve your close rate.

And yes! Don’t forget about the post-hiring evaluations. Many recruiters think their job ends the moment says yes to a role. However, once you have closed a role you can then ask the developer for feedback and improve your data-driven recruiting process. Or, you can look at the segments of the funnel where you think you lost time and figure out to make those time sinks disappear.

Tech recruiting is known to be tedious, and I hope these tips will help you make the long hours more productive. Happy hiring!

I Used AI to Build a "Simple Image Carousel" at VibeCodeArena. It Found 15+ Issues and Taught Me How to Fix Them.

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My Learning Journey

I wanted to understand what separates working code from good code. So I used VibeCodeArena.ai to pick a problem statement where different LLMs produce code for the same prompt. Upon landing on the main page of VibeCodeArena, I could see different challenges. Since I was interested in an Image carousal application, I picked the challenge with the prompt "Make a simple image carousel that lets users click 'next' and 'previous' buttons to cycle through images."

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Within seconds, I had code from multiple LLMs, including DeepSeek, Mistral, GPT, and Llama. Each code sample also had an objective evaluation score. I was pleasantly surprised to see so many solutions for the same problem. I picked gpt-oss-20b model from OpenAI. For this experiment, I wanted to focus on learning how to code better so either one of the LLMs could have worked. But VibeCodeArena can also be used to evaluate different LLMs to help make a decision about which model to use for what problem statement.

The model had produced a clean HTML, CSS, and JavaScript. The code looked professional. I could see the preview of the code by clicking on the render icon. It worked perfectly in my browser. The carousel was smooth, and the images loaded beautifully.

But was it actually good code?

I had no idea. That's when I decided to look at the evaluation metrics

What I Thought Was "Good Code"

A working image carousel with:

Clean, semantic HTML
Smooth CSS transitions
Keyboard navigation support
ARIA labels for accessibility
Error handling for failed images

It looked like something a senior developer would write. But I had questions:

Was it secure? Was it optimized? Would it scale? Were there better ways to structure it?

Without objective evaluation, I had no answers. So, I proceeded to look at the detailed evaluation metrics for this code

What VibeCodeArena's Evaluation Showed

The platform's objective evaluation revealed issues I never would have spotted:

Security Vulnerabilities (The Scary Ones)

No Content Security Policy (CSP): My carousel was wide open to XSS attacks. Anyone could inject malicious scripts through the image URLs or manipulate the DOM. VibeCodeArena flagged this immediately and recommended implementing CSP headers.

Missing Input Validation: The platform pointed out that while the code handles image errors, it doesn't validate or sanitize the image sources. A malicious actor could potentially exploit this.

Hardcoded Configuration: Image URLs and settings were hardcoded directly in the code. The platform recommended using environment variables instead - a best practice I completely overlooked.

SQL Injection Vulnerability Patterns: Even though this carousel doesn't use a database, the platform flagged coding patterns that could lead to SQL injection in similar contexts. This kind of forward-thinking analysis helps prevent copy-paste security disasters.

Performance Problems (The Silent Killers)

DOM Structure Depth (15 levels): VibeCodeArena measured my DOM at 15 levels deep. I had no idea. This creates unnecessary rendering overhead that would get worse as the carousel scales.

Expensive DOM Queries: The JavaScript was repeatedly querying the DOM without caching results. Under load, this would create performance bottlenecks I'd never notice in local testing.

Missing Performance Optimizations: The platform provided a checklist of optimizations I didn't even know existed:

No DNS-prefetch hints for external image domains
Missing width/height attributes causing layout shift
No preload directives for critical resources
Missing CSS containment properties
No will-change property for animated elements

Each of these seems minor, but together they compound into a poor user experience.

Code Quality Issues (The Technical Debt)

High Nesting Depth (4 levels): My JavaScript had logic nested 4 levels deep. VibeCodeArena flagged this as a maintainability concern and suggested flattening the logic.

Overly Specific CSS Selectors (depth: 9): My CSS had selectors 9 levels deep, making it brittle and hard to refactor. I thought I was being thorough; I was actually creating maintenance nightmares.

Code Duplication (7.9%): The platform detected nearly 8% code duplication across files. That's technical debt accumulating from day one.

Moderate Maintainability Index (67.5): While not terrible, the platform showed there's significant room for improvement in code maintainability.

Missing Best Practices (The Professional Touches)

The platform also flagged missing elements that separate hobby projects from professional code:

No 'use strict' directive in JavaScript
Missing package.json for dependency management
No test files
Missing README documentation
No .gitignore or version control setup
Could use functional array methods for cleaner code
Missing CSS animations for enhanced UX

The "Aha" Moment

Here's what hit me: I had no framework for evaluating code quality beyond "does it work?"

The carousel functioned. It was accessible. It had error handling. But I couldn't tell you if it was secure, optimized, or maintainable.

VibeCodeArena gave me that framework. It didn't just point out problems, it taught me what production-ready code looks like.

My New Workflow: The Learning Loop

This is when I discovered the real power of the platform. Here's my process now:

Step 1: Generate Code Using VibeCodeArena

I start with a prompt and let the AI generate the initial solution. This gives me a working baseline.

Step 2: Analyze Across Several Metrics

I can get comprehensive analysis across:

Security vulnerabilities
Performance/Efficiency issues
Performance optimization opportunities
Code Quality improvements

This is where I learn. Each issue includes explanation of why it matters and how to fix it.

Step 3: Click "Challenge" and Improve

Here's the game-changer: I click the "Challenge" button and start fixing the issues based on the suggestions. This turns passive reading into active learning.

Do I implement CSP headers correctly? Does flattening the nested logic actually improve readability? What happens when I add dns-prefetch hints?

I can even use AI to help improve my code. For this action, I can use from a list of several available models that don't need to be the same one that generated the code. This helps me to explore which models are good at what kind of tasks.

For my experiment, I decided to work on two suggestions provided by VibeCodeArena by preloading critical CSS/JS resources with <link rel="preload"> for faster rendering in index.html and by adding explicit width and height attributes to images to prevent layout shift in index.html. The code editor gave me change summary before I submitted by code for evaluation.

Step 4: Submit for Evaluation

After making improvements, I submit my code for evaluation. Now I see:

What actually improved (and by how much)
What new issues I might have introduced
Where I still have room to grow

Step 5: Hey, I Can Beat AI

My changes helped improve the performance metric of this simple code from 82% to 83% - Yay! But this was just one small change. I now believe that by acting upon multiple suggestions, I can easily improve the quality of the code that I write versus just relying on prompts.

Each improvement can move me up the leaderboard. I'm not just learning in isolation—I'm seeing how my solutions compare to other developers and AI models.

So, this is the loop: Generate → Analyze → Challenge → Improve → Measure → Repeat.

Every iteration makes me better at both evaluating AI code and writing better prompts.

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What This Means for Learning to Code with AI

This experience taught me three critical lessons:

1. Working ≠ Good Code

AI models are incredible at generating code that functions. But "it works" tells you nothing about security, performance, or maintainability.

The gap between "functional" and "production-ready" is where real learning happens. VibeCodeArena makes that gap visible and teachable.

2. Improvement Requires Measurement

I used to iterate on code blindly: "This seems better... I think?"

Now I know exactly what improved. When I flatten nested logic, I see the maintainability index go up. When I add CSP headers, I see security scores improve. When I optimize selectors, I see performance gains.

Measurement transforms vague improvement into concrete progress.

3. Competition Accelerates Learning

The leaderboard changed everything for me. I'm not just trying to write "good enough" code—I'm trying to climb past other developers and even beat the AI models.

This competitive element keeps me pushing to learn one more optimization, fix one more issue, implement one more best practice.

How the Platform Helps Me Become A Better Programmer

VibeCodeArena isn't just an evaluation tool—it's a structured learning environment. Here's what makes it effective:

Immediate Feedback: I see issues the moment I submit code, not weeks later in code review.

Contextual Education: Each issue comes with explanation and guidance. I learn why something matters, not just that it's wrong.

Iterative Improvement: The "Challenge" button transforms evaluation into action. I learn by doing, not just reading.

Measurable Progress: I can track my improvement over time—both in code quality scores and leaderboard position.

Comparative Learning: Seeing how my solutions stack up against others shows me what's possible and motivates me to reach higher.

What I've Learned So Far

Through this iterative process, I've gained practical knowledge I never would have developed just reading documentation:

How to implement Content Security Policy correctly
Why DOM depth matters for rendering performance
What CSS containment does and when to use it
How to structure code for better maintainability
Which performance optimizations actually make a difference

Each "Challenge" cycle teaches me something new. And because I'm measuring the impact, I know what actually works.

The Bottom Line

AI coding tools are incredible for generating starting points. But they don't produce high quality code and can't teach you what good code looks like or how to improve it.

VibeCodeArena bridges that gap by providing:

✓ Objective analysis that shows you what's actually wrong
✓ Educational feedback that explains why it matters
✓ A "Challenge" system that turns learning into action
✓ Measurable improvement tracking so you know what works
✓ Competitive motivation through leaderboards

My "simple image carousel" taught me an important lesson: The real skill isn't generating code with AI. It's knowing how to evaluate it, improve it, and learn from the process.

The future of AI-assisted development isn't just about prompting better. It's about developing the judgment to make AI-generated code production-ready. That requires structured learning, objective feedback, and iterative improvement. And that's exactly what VibeCodeArena delivers.

Here is a link to the code for the image carousal I used for my learning journey

#AIcoding #WebDevelopment #CodeQuality #VibeCoding #SoftwareEngineering #LearningToCode

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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:

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.

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.

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 or Hostinger Horizons 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.

Data-Driven Recruiting: All You Need To Know

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