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This Is Recruiting: Increasing Your Diversity Hiring ROI

This Is Recruiting: Increasing Your Diversity Hiring ROI

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Kumari Trishya
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May 26, 2021
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3 min read
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The ‘kickoff meeting’ is an important part of technical hiring. It is also usually the first time that a recruiter and a tech hiring manager get together to discuss an open requirement.

Now, let’s think about what has gone behind the scenes of this meeting. The technical hiring manager has had weeks; probably even months, of asking for approvals for this role to be opened up. They have waited for paperwork to be done, for budgets to be finalized, and are now at the table hoping the recruiter in front of them will help them fill this role URGENTLY.

The recruiter in question has come in prepared to turn this role into a diversity hiring opportunity, and has perhaps a whole presentation about strategy and tactics.

It’s a 30 minute conversation; perhaps even lesser. There is no way that a recruiter can walk into that meeting and convince a hiring manager who has an urgent requirement to throw everything they know about finding the right candidate outside the window, and use a fresh approach.

This is not a random prophecy. John Vlastelica, founder of Recruiting Toolbox, well knows this to be a fact. He has been in enough kickoff meetings to know that urgency triumphs over other values. Every single time.

So what does the modern recruiter do?

Tech has always had a diversity problem. The industry acknowledges it and we know recruiters are getting more aware of how they can help reduce the gap. The smart recruiter, however, does not wait till the kickoff meeting to effect a change. The smart recruiter creates a strategy where diversity is baked into the company’s hiring practices from the get go.As always, this is easier said than done. Here are some of the tactics John has found to be useful when creating an effective diversity hiring process and improving diversity hiring ROI:

#1. Farm your talent pool, as opposed to hunting

Diversity hiring is not just about going to, say, a historically Black university and organizing a hiring drive to get more African American employees on board. That is what recruiters do when they ‘hunt’ for talent. Investing in these diverse student groups, and keeping them engaged even when you are not actively sourcing is what John calls ‘farming’.Salesforce’s Pathfinder Program is one of the examples of how companies can invest in and cultivate relationships with future talent. Such initiatives provide commitment as well as opportunity for companies to engage with marginalized communities, and build a robust pipeline of diverse IT talent.For this to happen successfully, recruiters need to be prepared. Opportunity is relative to several parameters like location, race, age, and gender. Use tools like LinkedIn talent and SeekOut to understand what the supply of talent looks like when viewed through the lens of diversity, and then create talent ‘farming’ initiatives that will help you create more opportunities where they matter. A data-driven pre-sourcing strategy like this would also make it easier for you to justify remote hiring or a relocation - both of which can enable you to hire a more diverse workforce.

#2. Bust the myth of the ‘perfect candidate’

Have you ever read a job description for a tech role? Most of the time, it is full of jargon and a near-impossible list of must-haves. This ‘wishlist’ of skill sets creates a very narrow target profile, and makes our job as recruiters even harder.Every time a recruiter says yes to such a hiring requirement, they give away the power to hire well to someone else. Instead, ask for a conversation with your hiring manager. List down the actual - and realistic - list of ‘must haves’. These are non negotiable. Then, make a list of ‘adjacent skills’ which can either be fulfilled by someone else in the team, or can be foregone when hiring.If hiring was like ordering a good burger, then your must haves would be the bun, the meat, the onion and lettuce, cheese, and the sauce that goes on the meat. You wouldn’t say no to a good burger just because it didn’t come with five additional dips, would you?Tech hiring simplifiedBusting the myth of the ‘perfect hire’ also keeps the hiring centered on skills, and not a bunch of keywords. Some skills are trainable, and a good candidate would be able to learn them on the job easily. More and more hiring managers are realizing this, and trying to hire generalists who have a hunger to learn and upskill, instead of chasing pedigree. As recruiters, it is our job to ensure our managers know the value of a candidate who is adaptable and quick to learn.So, instead of trying to hire the mythical ‘ideal hire’, widen the aperture to create multiple success profiles for each role and share them with your hiring manager. Then go back to the talent ‘farm’ you created in Step 1, and find people who fit these multiple success profiles.

#3. Train. Talk. Tweak.

Biases can creep into any hiring environment. With so many of our meetings happening over video now, in non-professional settings, it has become even easier to judge someone for the art on their wall, or their choice of pet. These biases can cause all your best-laid plans to go awry. Hence, the need for frequent communication and training.
Hiring managers need to be aware of their own subconscious biases in different scenarios. They need to be provided with the right training and tools to beat these biases. Hiring managers also need to understand and learn to create inclusive interview settings and prioritize candidate experience. John suggests a quarterly health check between recruiters and managers to stay on top of these issues.
It’s also important for managers to understand that some of this talking and training and tweaking will affect the speed of hiring. Speed is the love language of hiring managers, and John says he has never met a hiring manager who didn’t want a role to be filled yesterday. However, with diversity as the main focus, speed is not always possible. At least, in the initial stages when you are still perfecting your strategies.Sometimes, putting the brakes on isn’t that bad, right?
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#4. Set realistic goals to improve your diversity hiring ROI

Diversity hiring isn’t a one off. It is a continuous process, and John suggests that you have very realistic benchmarks for your team. Look at Google for instance. One of the biggest IT giants in the world with all the resources at its disposal has not been able to crack diversity hiring at scale. It’s difficult, that is why!You’re not Google. So, don’t begin by setting yourself up for failure. Expectation and goal setting is very important here, as is measuring progress. Don’t forget to include your people leaders and tech managers when setting goals, but also do not accept unachievable success standards.

Google Diversity Report

Source: Google Diversity Report 2020

The bottom-line for better diversity hiring: Look for real improvements

A few years ago, Deloitte started the practice of matching new hires with a ‘career coach’ to understand the issues minority technologists face in the organization in their first two years. Now, this is real improvement.Metrics, charts, numbers are a good measure of progress, but they don’t paint the whole picture accurately. While you keep track of these, don’t take your eye off the bigger goal. Train your managers to recognize practices that are meant to screen out potential hires. Create inclusive interviewing and engagement processes. Effect change at the grassroots so that diversity is included in all your pre-sourcing activities, instead of waiting for that job requirement to land at your table.Real change may come slowly, but the diversity hiring ROI of these efforts is more long-term. And that’s the only ‘ideal’ that all of us should be chasing! ****

Learn more about bettering your diversity hiring ROI with John below:

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Author
Kumari Trishya
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May 26, 2021
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3 min read
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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 code.

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.

How Candidates Use Technology to Cheat in Online Technical Assessments

Impact of Online Assessments in Technical Hiring


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.



According to research, nearly 30 to 50 percent of candidates cheat in online assessments for entry level jobs. Even 10% of senior candidates have been reportedly caught cheating.

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


  1. 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
  2. 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
  3. 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
  4. 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.
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