Revamping Data Science Interviews

Introduction

The goal of interviews is to assess a candidate’s skill sets, fit, and potential to achieve impact. Well-designed interviews can reduce both false positives (bad hires) and false negatives (rejecting good candidates). On the former, for a data science (DS) role, false positive risk can entail a candidate not understanding how to frame technical problems (i.e. translating business objectives into technical solutions), lacking technical expertise (e.g. statistics, machine learning (ML), analytics, and/or data engineering), or having difficulty collaborating with others.

For a DS organization, maintaining an effective interview process is pivotal for assessing and attracting talent. At a high level, better interviews can lead to better hires. This, in turn, can improve job satisfaction, create better retention, and build more mutual trust in the company and employees – resulting in an overall stronger organizational reputation.

Beyond the aforementioned reasons, there are numerous other considerations that might motivate a DS org to revamp/update its interview process from time to time:

• Changing Business Needs: Startups typically require generalists (i.e. to quickly ship, adapt, and put out fires as they arise), whereas mature companies typically require many more specialists. The interview process should therefore evolve alongside business complexity.

• Broader Problem Bank: A large DS org benefits from having backup sets of interviews, as well as alternatives for different business areas (e.g. Product, GTM, Platform, Infrastructure, etc.). Creating new problems can protect the company from potential question leaks, as well as attract an even wider degree of talent depending on the hiring manager/team’s most critical needs.

• Reputation + Effectiveness: A company with a repetitive or ineffective interview process risks missing strong candidates and discouraging top talent. If interviews are designed thoughtfully, then they can help convince a candidate that interviewers are the types of peers with whom they eventually want to work.

• Generative AI: For well-structured problems, tools like ChatGPT make technical implementation much easier and faster, reducing the effectiveness of syntax-heavy questions. As a result, for DS interviews, many companies are gradually replacing standard problems with more open-endedness, and focusing more on candidates’ ability to gather context, assess trade-offs, collaborate well with the interviewer, etc.

• Fun/Learning: Crafting interviews can help interviewers themselves grow and learn - much like how students can often deepen their understanding by writing tests or problem sets. 

Updating interviews helps to refine what the needs of the business are for the DS role. In this blog post, we will discuss some general good practices that a DS organization may wish to consider when carrying out these updates/redesigns.

Note: We will focus primarily on DS interview redesign for individual contributor (IC) roles, though similar considerations apply also to revamping DS manager screens (which have further emphasis on people management experience).

A Brief DS Interview Process Overview

Most DS interviews can be categorized as technical or behavioral:

• Technical typically involves:

  • Pair programming challenges: e.g. using SQL and/or Python;

  • Technical case studies about a specific core competency, such as:

    • ML;

    • Statistics and Probability;

    • Exploratory Data Analysis;

    • Data Engineering.

• Behavioral usually revolves around leadership, influence, communication, strategic thinking, etc. These can also be a way for hiring managers and stakeholders to better get to know the candidate and their relevant experience level.

• Some sessions blend both, such as:

  • Technical DS project presentations, which can give an opportunity to showcase technical expertise as well as end-to-end ownership, influence, collaboration, and business impact. 

  • Take-home case studies, which assess technical expertise as well as the ability to tackle open-ended questions, communicate effectively, and make effective recommendations.

Framework for a DS Interview Revamp

Revamping existing interviews in a DS organization requires a thoughtful, structured approach to achieve ideal outcomes. To successfully overhaul the interview process, it is crucial to define the problem, gather support from key stakeholders, and create a system that can be continuously refined. Below are some key steps to guide you through the process:

• Align on the Problem:

  • Before developing new interviews, it is important to establish why the changes are necessary. This helps to anchor the work and provide direction when ambiguity arises later on. Common reasons for a revamp include:

    • Outdated content;

    • Internal skill gaps;

    • Question leaks;

    • Redundancy or inefficiencies;

    • Lack of scalability.

• Get Buy-in From Leadership:

  • Identify the person(s) who have the final stamp of approval and ensure that they endorse the project. These decision-makers must support the effort, including the transition in the interim, as you do not want to do extensive work on interviews only to find out that no one actually wanted them to change!

• Form the Working Group:

  • Gather a mix of DS ICs and managers in key domain areas (e.g. Product, GTM, Platform, Infrastructure, etc.) who are interested. Their credibility can help drive future adoption. Ideally, working group members should be methodical, thoughtful, responsive, and genuinely passionate about strengthening the DS community.

• Identify Specific Gaps and Challenges: 

  • Often, it is helpful to conduct a survey to allow folks from the entire DS organization (including some key business partners) to provide feedback on limitations of the current interview set. Take the opportunity to sketch out attributes of strong candidates and the high-level rubrics needed to assess them (also, think about broader industry standards here).

• Draft the Plan: 

  • Writing out plans is crucial, so that everyone is clear and onboard with what each interview should evaluate and what changes need to be made. For example, in addition to interview content:

    • Does the order of interviews matter?

    • What interview formats do we want to use?

    • Are the interviews for all levels of the role?

This summary doc can be shared (to approvers and other interested managers) so that leadership can give input before everyone is working to create the drafts. It can also help capture some institutional memory for the next iteration.

• Creating Interview Questions:

  • Once the rubric and plan are set, start formulating concrete interview rounds. We want a workable draft that fits the time, difficulty, specific focus areas (e.g. ML), and a rubric that clearly evaluates key areas. Ideally, anchor on real-world problems that have been of recent interest for the organization (e.g. analyzing the incremental lift of a recent pricing experiment, defining success metrics for a new online marketplace initiative). Also, prioritize essential vs. “nice to have” skills.

  • At a high level, a good interview should be strong at all of the following:

    • Evaluating core skill sets, both technical and non-technical;

    • Difficulty (i.e. is this a reasonable length for the given time);

    • Clarity (can someone without a specific background who has the right skill set understand this?);

    • Uniqueness (i.e. is this like every other screen? And is it unique to THIS business?);

    • Flexibility to gather signals of deeper expertise/potential for more senior candidates (wherever applicable);

    • Flexibility to gather signals about particular company operating principles (if applicable).

  • More details in the Appendix.

• Internal Practice Runs: 

  • Once the new interviews start to take shape, conduct 2-3 practice runs, ideally with a diverse group of DS members and a shadower present. Treat these like real scheduled interviews, but focus on providing feedback on the interview itself, not on evaluating peers.

  • Refine wording and clarity – no one wants a skilled candidate to fail an interview because the prompt was too complex and unclear. Ensure that folks across different contexts understand (e.g. if the interview is for a marketing role and the question is about a platform space – will the different context make it too difficult to get a good read on the candidate?).

  • Consider also doing practice runs with individuals who are unlikely to perform well (e.g. someone without significant ML experience taking an ML interview), as a further signal for gauging difficulty.

  • Confirm that interviews are doable with commonly used industry tool sets, wherever applicable (e.g. for a coding screen, while Python might be preferred, R and other languages (e.g. Java/Scala) should be permissible)!

  • Use practice runs to help calibrate interviewers. Sometimes, getting it right just takes practice, and there is no perfect wording.

• Final Leadership Review:

  • If leadership is kept in the loop throughout, then this should be a formality.

• Live Runs:

  • Get a hiring manager to volunteer to try out new interviews on an open headcount (also, ask recruiting partners for opportunities and keep them closely in the loop). Update any materials sent to candidates beforehand and ensure there are shadowers and “reverse shadowers” to help interviewers get calibrated. It helps to start with people who gave or took the internal test runs. Make sure to get any final edits in before finalizing an interview (usually, 2-3 more live runs should give sufficient confidence).

• Broader Training and Adoption:

  • Conduct a final training so that folks outside of those who shadowed or are calibrated are caught up on the changes and new interviews. If necessary, do a followup Q&A (that also includes the hiring decision-makers) to make sure everyone is clear on all the changes. This is a good opportunity to refresh interview skill sets in general, so maybe even include a FAQ.

• Deprecate the Previous Interviews:

  • This is the final step to ensure a smooth transition. The previous interviews can potentially be kept as backups, but make sure to have consistency for each role. Open roles that used the old interviews should ideally finish with them, but new open roles should all use the new interviews.

  • Once again, remember to always keep recruiting partners up to date!

• Create Different Variations:

  • Once a new format is adopted, adding other versions can help create variety. For example, if you have an ML interview version using churn, try one for fraud/risk! However, be sure to use the same generalizable rubric/structure so they are effectively interchangeable.

Interview Update Effectiveness

A successful revamp depends on the specific goals and principles of the new interview creation, but in general, it is helpful to monitor offer acceptance rates/hiring success after 3-6 months, time to hire, pass rates, and feedback from experienced DS interviewers. The goal is not necessarily to increase or decrease pass rates, since they can be affected by noise and other factors such as the broader competitive landscape.

Tracking pass rates can be helpful to ensure consistency across different question variations (i.e. there are no “easy” or “hard” versions of the same interview). If some question variations have much lower or higher pass rates, then it can be time to adjust the difficulty or rubric, something to continuously keep an eye on, especially in cases where an interview set is leaked.

In practice, interviewing falls a bit on a distribution. Ideally, if everyone who is calibrated conducted an interview on the same candidate, then they would come to the same conclusion. In reality, clear wording, good pacing, and experience can reduce the likelihood of different outputs for the same input – but it can never be eliminated entirely.

Conclusion

Interviews are not just about managers selecting candidates – they shape the entire company by determining who joins and influences its future. Ideally, the team should feel a sense of pride and ownership in the new interview set, which can now better detect candidates who would struggle in key areas addressed during the problem alignment phase. Indeed, if a DS organization upskilled the technical rigor evaluation, then it might start to see more technical projects being proposed/pursued over time. However, if different problems start to arise, then it might need to critically examine interview feedback/outcomes and re-attune to the needs of the business. Ultimately, DS interview revamp is an ongoing process and never truly reaches perfection and can quickly lag for a growing or changing company.

A good rule of thumb might be to re-evaluate and update interviews every 2-3 years, or whenever recurring hiring challenges emerge. However, ongoing vigilance (e.g. for leaked interviews) and continuous feedback are just as important. In a fast-moving startup, this process might take a month. In a larger company, it could take up to 6 months to align stakeholders, build principles, test variations, and provide training for full adoption.

At the end of the day, revamping DS interviews is not just about improving hiring outcomes – it is about strengthening the entire DS function. Thoughtful, well-calibrated interviews ensure that new hires bring the right mix of skills that align with the business needs. By continuously refining the process, a DS organization not only improves hiring efficiency, but also shapes the future of the team and broader company. While this work takes time and effort, the long-term impact is well worth it!

Appendix

While the main focus of this blog post was on revamping interviews in a DS organization, we also list a few guidelines for organizations to consider when creating and structuring their overall DS interview process:

Interview Creation Guidelines:

Below are a key few considerations around DS interview creation.

• Once you have a general idea of the problem space, break down the steps to solve the problem (e.g. for an applied ML problem related to binary classification, how do we obtain and define the labels?) and map the steps to a rubric. Balance open-ended questions like “suggest further analysis” with pass-fail questions. Reduce ambiguity and bias with a clear rubric (e.g. “speak reasonably about churn” is vague, but “break out by self-serve vs. sales-led” is more specific).

• While not required, a common structure is to have a number of “pass-fail” questions where a candidate needs above a certain amount correct. When writing these (particularly for a larger company), aim for an outcome that would generally be the same regardless of who conducts the interview. For inspiration, look around at other companies and maybe even interview to see what their process is like!

• A note on leveling: For a more established DS org at a more mature company, this process would also want to think about leveling, including who can conduct the interviews. If we only want our best hires to conduct interviews, we would take more of their time and slow down the process – but if we do not have a good standard, we could also quickly dilute our talent pool with a lower bar.

Structuring the DS Interview Loop

When designing DS interviews, balancing behavioral and technical rounds can be challenging. Having more interview sessions/topics can increase confidence, but it also increases interview duration, and hence overall “cost.”

• Early Screening (After Resume)

  • A generalist knowledge screen (covering statistics and programming fundamentals) helps assess the likelihood of onsite success without needing to specialize early on in the loop. For this type of loop, the priority is to test for table stakes understanding before investing in a full on-site round.

  • A hiring manager screen that dives into past experience, and more directly assesses fit and overall potential, but also indirectly assess technical expertise via appropriate probing questions.

• Technical Skill Assessment

  • Coding challenges should prioritize critical thinking over memorization. Ideally, there should be multiple parts that increase in difficulty, each one expanding upon the previous part, but can also be solved independently (i.e. even if no progress was made on the earlier part).

  • Onsite technical interviews should align to the specific needs of the role (e.g. more ML for more ML-heavy DS, more causal inference/experimentation for inference-heavy roles, more product analytics for analytics-heavy roles, etc.). If the role is generalist in nature, it might make sense to include all or most of the above, but more so to assess overall capability rather than a specific skill set.

  • Questions should be tied to applied, real-world questions and not be overly theoretical or rely heavily on memorization. It is quite reasonable for technical rounds to draw from specific domains, such as fraud detection, search/recommendations, marketing, customer support, online marketplaces, etc., but the setup and assumptions should be describable within several sentences, and not heavy on jargon or overly specialized knowledge.

  • A good technical interview should, wherever possible, be designed to provide a sneak peak into what the underlying DS job could be like, and give an indication of whether the interviewer/peer trusts that the candidate could conduct the job alone without supervision (i.e. validating their own work) and find valuable insights. 

• Behavioral Interviews

  • These can help assess how a candidate collaborates with cross-functional stakeholders. For DS this might include engineers, product managers (PMs), marketing/sales, finance and strategy (F&S), etc. 

  • In the industry, there are various standard approaches for behavioral rounds (which apply to all roles, not just to DS), such as getting candidates to adopt the STAR method to describe their past experiences, and to showcase influence, communication, strategic alignment with partners, dealing with conflict, etc.

  • In addition to evaluating background/fit, behavioral rounds should check for signals on whether the candidate exhibits intellectual honesty, a customer-centric mindset, humility, focus on business impact, etc., as well as any other company operating principles that may exist for the hiring organization.

Each interview needs to be informative and decisive, with relatively objective and quantifiable criteria. That means every interview round moves towards or away from an offer, not just sideways to check a box.

Co-author

Rob Wang