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The Importance of Assessing Interpersonal Skills in Recruitment

The Importance of Assessing Interpersonal Skills in Recruitment

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Nischal V Chadaga
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December 5, 2024
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6 min read
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In the competitive world of hiring, particularly in tech, recruitment is no longer just about finding candidates with the right technical expertise. Interpersonal skills—often termed “soft skills“—have become equally critical, especially as organizations increasingly emphasize teamwork, communication, and adaptability. For tech teams tasked with solving complex problems, interpersonal skills ensure smoother collaboration, innovation, and productivity.

Why interpersonal skills matter in tech hiring ?

Tech roles are rarely performed in isolation. Whether a software developer collaborates with product managers or a data scientist works alongside stakeholders to translate business requirements, the ability to communicate effectively is non-negotiable. Here are a few reasons why interpersonal skills are essential in tech hiring:

  1. Team collaboration: Most tech projects require cross-functional teams working together. A backend developer might need to sync with frontend teams or designers to align on project goals. Strong interpersonal skills foster open communication and reduce misunderstandings.
  2. Client interaction: Many roles involve engaging with clients to understand their needs and pain points. Without the ability to communicate clearly and empathize, delivering client-centric solutions becomes challenging.
  3. Conflict resolution: Tech environments can be high-pressure. Conflicts or differing opinions are inevitable. Candidates with strong interpersonal skills can navigate these challenges constructively, ensuring that team dynamics remain intact.
  4. Leadership and mentorship: For mid-level or senior tech roles, mentoring junior employees is often part of the job. Effective mentors not only guide but also inspire, requiring a blend of empathy, clarity, and active listening.

Challenges in assessing interpersonal skills

Assessing interpersonal skills during recruitment can be more complex than evaluating technical capabilities. Unlike technical skills, which can be objectively measured through coding tests or problem-solving challenges, interpersonal skills are subjective and context-dependent. This makes them harder to quantify, and the risk of overlooking or misjudging these skills is high.

Below are some of the key challenges, with examples to illustrate their real-world implications:

1. Subjectivity in evaluation

Interpersonal skills, such as communication, empathy, and conflict resolution, can vary significantly based on personal interpretation. What one interviewer perceives as “assertiveness” might come across as “aggressiveness” to another. This subjectivity can lead to inconsistent evaluations.

  • Example: During an interview, a candidate may confidently explain their role in resolving a team conflict. An empathetic interviewer might view their approach as collaborative, while another interviewer might see it as overstepping boundaries. Such differences can result in biased assessments.

2. Limited interaction time

Interviews and assessments offer limited opportunities to observe a candidate’s behavior. Most interpersonal skills manifest over time and in various contexts, which are difficult to replicate in a short interview process.

  • Example: A candidate might perform well in a calm, structured interview environment but struggle to collaborate effectively in high-pressure, real-world scenarios like product launches or tight deadlines.

3. Overreliance on self-reported skills

Candidates often highlight their interpersonal skills on resumes or during interviews, but these self-reported claims may not reflect reality. Without corroboration, it’s easy to overestimate these skills.

  • Example: A candidate may claim to have excellent teamwork skills but might have been the sole decision-maker in previous roles. Their ability to share responsibilities and collaborate under differing viewpoints might be untested.

4. Difficulty simulating real-world scenarios

Interpersonal skills often shine (or falter) in dynamic, real-world situations that involve pressure, ambiguity, and team interactions. Recreating such environments during interviews is challenging.

  • Example: While a pair programming task can demonstrate technical collaboration, it doesn’t necessarily replicate the complex dynamics of resolving a disagreement between team members during a product sprint.

5. Bias in group interactions

When assessing interpersonal skills through group exercises or discussions, bias can skew results. Candidates who are naturally more extroverted may dominate the conversation, overshadowing quieter candidates who could still be effective team players.

  • Example: In a hackathon-style assessment, an extroverted candidate might take the lead, presenting themselves as a strong collaborator, while introverted but equally capable individuals may not get the same recognition.

6. Overemphasis on cultural fit

While ensuring cultural alignment is essential, overemphasizing it can sometimes exclude diverse candidates whose interpersonal skills might shine in different team dynamics or work cultures.

  • Example: A company that values open, verbal communication might undervalue a candidate who communicates effectively through written documentation or quieter, one-on-one interactions, missing out on a valuable contributor.

7. Lack of standardized metrics

Interpersonal skills are inherently difficult to measure, and many organizations lack standardized methods or benchmarks for assessing them. Without clear criteria, evaluations can be inconsistent and unreliable.

  • Example: Two hiring managers might assess the same candidate differently based on vague questions like “Do you think they’ll fit in?” instead of structured evaluations tied to specific interpersonal behaviors.

Best practices for assessing interpersonal skills in tech interviews

Effectively assessing interpersonal skills in tech hiring requires thoughtful strategies that go beyond generic interview questions. These methods should provide clear insights into how candidates communicate, collaborate, and adapt in real-world scenarios. Below are some proven best practices, along with examples from tech interviews, to ensure a holistic evaluation of interpersonal skills.

1. Behavioral interviews

Behavioral interviews focus on past experiences to predict future performance. They are particularly effective for assessing interpersonal skills like teamwork, conflict resolution, and leadership.

  • Structured interviews: Use a consistent format to ask every candidate specific questions about their interpersonal skills.
  • Example: “Tell me about a time when you had to resolve a conflict between team members. What was your approach, and what was the outcome?”
    • Why it works: This question reveals how the candidate handles disputes, listens to others, and navigates team dynamics.
  • STAR Method: Encourage candidates to structure responses using the STAR method (Situation, Task, Action, Result).
  • Example response: A candidate might describe a project where conflicting deadlines caused tension in the team. They could explain how they mediated between developers and product managers, re-prioritized tasks, and ensured timely delivery, showcasing conflict resolution and collaboration.
  • Open-ended questions: Let candidates elaborate on their experiences and thought processes.
  • Example: “How do you approach giving constructive feedback to a teammate who isn’t meeting expectations?”

2. Technical interviews with a human touch

While technical interviews primarily assess problem-solving abilities, they are also opportunities to evaluate interpersonal skills.

  • Communication skills: Observe how candidates explain their thought processes during coding challenges.
    • Example: In a live coding interview, a candidate might explain their logic for implementing an algorithm step by step. Clear, logical communication demonstrates their ability to work with others and articulate complex ideas effectively.
  • Collaboration: Use pair programming exercises to see how well candidates collaborate.
    • Example: Ask the candidate to solve a problem with an interviewer acting as a teammate. Observe how they share ideas, consider input, and adapt to suggestions. Candidates who dominate the session or dismiss input might lack essential teamwork skills.
  • Explanatory skills: Assess candidates’ ability to simplify technical concepts for non-technical audiences.
    • Example: “Imagine you’re explaining how machine learning works to a client with no technical background. How would you describe it?” Strong candidates use analogies or simple language to make technical topics accessible.

3. Team-based exercises

Simulated team exercises provide a closer look at how candidates interact in group settings.

  • Hackathons or Group challenges: Include a collaborative task during the hiring process, such as building a prototype or solving a complex problem as a team.
    • Example: During a hackathon, observe how candidates delegate tasks, handle disagreements, and support each other. A candidate who takes the time to help a teammate debug code demonstrates empathy and collaboration.
  • Case studies: Present a real-world problem requiring teamwork to resolve.
    • Example: Ask a group of candidates to design an architecture for a scalable web application. Observe who takes the lead, how they include others in the discussion, and how they balance differing opinions.

4. Cultural fit assessments

Cultural fit ensures that candidates align with the organization’s values and work style without sacrificing diversity.

  • Personality assessments: Use tools like Myers-Briggs or DISC to evaluate traits that complement the team’s dynamics.
    • Example: A DISC assessment might reveal that a candidate is an analytical problem-solver, helping the team identify potential gaps in group dynamics.
  • Cultural fit interviews: Incorporate questions that reveal candidates’ adaptability and alignment with organizational values.
    • Example: “What’s your preferred way of working: independent problem-solving or team collaboration? Why?” Responses provide insights into their adaptability and openness to diverse work styles.

5. Feedback and Reference checks

Use references and peer feedback to validate interpersonal skills.

  • Example questions for references:
    • “Can you describe how they handled disagreements or conflicts within the team?”
    • “How did they collaborate with colleagues in cross-functional roles?”
  • Peer interviews: Allow team members to interact with candidates and provide feedback.
    • Example: A candidate interviewing for a DevOps role might have a casual discussion with developers and project managers to gauge their ability to align different priorities.

💡Real-world example: How Google uses “Team Match” to find the right candidate

Google’s hiring process includes a “team match” phase where candidates meet potential teammates to assess compatibility. This step focuses on evaluating interpersonal skills, such as how candidates approach collaboration, communication, and shared goals. Team members provide feedback on how well the candidate might integrate into the team culture.

The tech edge: Why soft skills are the future

In an era where AI and automation are rapidly taking over repetitive technical tasks, soft skills like adaptability, creativity, and emotional intelligence have become key differentiators for tech professionals. Teams that blend technical brilliance with interpersonal finesse are better equipped to navigate complex projects, foster innovation, and maintain morale in fast-paced environments.

HackerEarth’s role in assessing a developer’s interpersonal skills

While HackerEarth is best known for its robust platform for technical assessments, it can also be leveraged to indirectly evaluate interpersonal skills:

  • Code reviews: Analyze how candidates approach problem-solving and provide constructive feedback during collaborative coding tasks. This showcases their ability to work with others effectively.
  • Technical interviews: Observe candidates’ communication skills and how they articulate their thought process when solving technical challenges. Clear explanations reflect their ability to collaborate with both technical and non-technical stakeholders.
  • Personality assessments: Incorporate tools to measure traits like adaptability, teamwork, and emotional intelligence—key indicators of strong interpersonal skills.

By integrating platforms like HackerEarth into the recruitment process, organizations can streamline the assessment of both technical and interpersonal competencies, ensuring they hire candidates who are not only skilled coders but also exceptional collaborators.

Tech teams thrive when technical brilliance meets interpersonal finesse. By prioritizing both, companies can build resilient, high-performing teams that drive sustainable success in an ever-evolving industry.

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Author
Nischal V Chadaga
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December 5, 2024
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6 min read
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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...

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...

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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