Rethinking Data Science Interviews within the Age of AI

AI is rewriting the day-to-day of knowledge scientists. , data scientists must learn improve productivity and unlock recent possibilities with AI. Meanwhile, this transformation also poses a challenge to hiring managers: find the perfect talent that may thrive within the AI era? One critical step in constructing a powerful AI-empowered data team is to revamp the hiring process to higher evaluate candidates’ ability to work alongside AI. 

In this text, I’ll share my perspective on how data scientist interviews should (would) evolve within the age of AI. While my focus here is on Data Scientist Analytics (DSA) roles, the ideas here also apply to other data positions, corresponding to Machine Learning Engineers (MLE). 

I. The Traditional Data Scientist Interview Loop

Before talking about how things will change, let’s undergo the present structure of knowledge scientist interviews. Except for the initial recruiter call and hiring manager screening, a typical data scientist interview process includes:

1. Coding interviews: SQL or Python coding inquiries to test syntax and basic logic.
2. Statistics interviews: Statistics and probability questions, in addition to essentially the most common statistical applications in data science workflows, corresponding to A/B testing and causal inference.
3. Machine learning interviews: Deep dive into machine learning algorithms, experiences, and cases.
4. Business case interviews: Discuss a hypothetical problem to check analytical considering and business understanding — metrics, funnels, growth, retention strategies, and analytical approaches.
5. Behavioral interviews: Standard “walk me through a project / a time while you XXX” to know how candidates handle specific situations and in the event that they are a cultural fit. 
6. Cross-functional interviews: Data Scientist is a technical role, but it’s also highly cross-functional, aiming to drive real business impact using data. Due to this fact, many data scientist interview loops today include a cross-functional interview round to speak with a business partner to evaluate the domain knowledge, communication skills, and stakeholder collaboration. 

From the list above, you may see that data scientist interviews often have an excellent mixture of technical and non-technical evaluations. But with AI entering the sport, a few of these interviews will change significantly, while some will turn out to be much more necessary. Let’s break it down.

II. How Interviews Will Shift within the Age of AI

In my view, how the interview loops are going to alter is determined by two things: 1. Can AI handle the duty quickly? 2. Does it tell how the candidate uses AI thoughtfully? 

Coding Interviews: Most More likely to Change First

What can AI do quickly? Easy coding tasks. Due to this fact, the coding interview might be the primary one to be impacted. 

Today’s coding interviews ask candidates to jot down SQL and Python code accurately. The SQL questions often require easy joins, CTEs, aggregations, and window functions. And the Python questions might be straightforward data manipulation with pandas and numpy, or easy LeetCode-style questions. But let’s be honest, these interview questions could be solved by AI easily today. In my article one 12 months ago, I evaluated how ChatGPT, Claude, and Gemini perform in easy SQL tasks, and was impressed already by all three — Claude 3.5 Sonnet even got full points in my test. 

Let’s take one step back. For data scientists, the true coding challenge today comes from 1. Understanding the information and locating the right tables and fields; 2. Translating your data questions into the right query/code. In other words, today’s coding interviews mostly test basic syntax, which is likely to be fair for entry-level candidates, but have been failing to guage actual problem-solving for a very long time, even without the evolution of AI. The proven fact that AI can answer them quickly only makes this round much more outdated. 

So, how can we make the coding interviews more meaningful? I believe, firstly, we should always allow candidates to make use of AI tools like GitHub Copilot or Cursor in the course of the coding interview to mimic the brand new work environment with AI. I even have seen this happening progressively within the industry. For instance, Canva introduced AI-assisted coding interviews recently, and Greenhouse also says, “” I believe allowing candidates to make use of AI is best than trying every means to forestall them from cheating with AI, as they are going to use (and are expected to make use of) AI at work anyway :). 

Meanwhile, as an alternative of asking easy SQL/Python questions, I even have a few ideas:

1. Ideally, we could arrange an environment with multiple documented tables and ask the candidates to do a live problem-solving session with the assistance of AI. As an alternative of asking questions like “write a question to calculate MAU since 2024”, ask more open-ended questions like “how would you investigate customer churn since 2024?”. The evaluation is not going to only be based on code accuracy, but additionally on how the candidates frame their evaluation and interpret the outcomes. And when the candidate interacts with the AI tool, how do they prompt, iterate, and evaluate the output. Though this does make interviewers’ lives harder — they are going to should be very conversant in the datasets and find a way to follow the candidates’ logic, ask follow-up questions, and assess the responses. 
2. Alternatively, we will ask candidates to guage the AI outputs — this might be easier to establish and fewer stressful and time-consuming than the above format. While AI can assist with coding, it’s still humans’ responsibility to guage the output. Not every AI-generated code is correct, even when it runs without errors. The interviewer can describe what they are attempting to do and show AI-generated code, then ask the candidates to discover if the logic is correct, if it ignores any edge cases, if there’s any higher alternatives, or if the code could be optimized further — this requires the candidate to totally understand translates between the business logic and the code. It is usually easier to design an ordinary rubric with this problem setup. 

Statistics and Machine Learning Interviews: Less Theory, More Context

Next, let’s discuss statistics and machine learning interviews. AI is an awesome teacher — it explains basic stats and machine learning concepts clearly and can assist brainstorm different methodologies — try asking ChatGPT, “explain p-value to me like I’m five”. Nonetheless, knowing the theories doesn’t at all times mean applying the suitable methods based on business scenarios. Yow will discover an excellent example in my Google Data Science Agent evaluation article — it does an awesome job establishing a modeling framework with functional starter code, nevertheless it requires a transparent problem statement and a clean dataset. Human expertise can be essential for feature engineering, selecting the perfect domain-specific data science practices, and tuning the models. Keeping that in mind, I believe statistics and machine learning interviews should ask fewer theoretical questions or coding models from scratch, but integrate more with business case interviews to check if the candidates can apply theories to a business context. So as an alternative of asking isolated questions like “What’s the difference between Ridge and Lasso Regression?” or “Easy methods to calculate the sample size for an A/B test?”, present a real-world problem and observe how the candidates approach the questions analytically, if the proposed methods make sense, and if they convey their ideas logically. It’s not like we not need the candidates to have solid stats and ML knowledge, but we’ll test the knowledge more seamlessly within the case discussion. For instance, when going through a hypothetical fraud detection case, we will ask why the candidate proposes XGBoost over Random Forest, and if it is best to impute missing values in household income because the median or zero.  

The excellent news is we’ve already seen a lot of these technical + business case interviews within the industry. My prediction is that AI will make it much more predominant.  

Behavioral & Cross-functional Interviews: Mostly Unchanged, But With Recent Twists

For the remaining two interview types, behavioral interviews and cross-functional interviews, they are going to likely stay here. They evaluate the candidates’ soft skills, corresponding to cross-functional collaboration, communication, conflict resolution, and ownership, in addition to their domain knowledge. These are the things AI cannot replace. Nonetheless, there might be some shifts in what questions people ask. Interviewers can add questions on the candidates’ past experience with AI tools to get more signal on how they use AI to spice up productivity and solve problems. For instance, a product manager might ask, “How can we use AI to enhance customer onboarding?” These conversations can surface the candidates’ ability to discover AI use cases that drive real business value.

Take-home Assignments: Still Controversial, But Useful

Besides these common interview formats, there’s also a controversial one which comes up in data science interview loops occasionally — Take-home assignments. It is often within the format of providing a dataset and asking the candidates to do an evaluation or construct a model. Sometimes there are guiding questions, sometimes not. Deliverables range from a Jupyter notebook to a sophisticated slide deck. 

I do know there are candidates who really hate it. It takes numerous effort — though recruiters at all times say average candidates take about 4 hours, the actual time you spend is often significantly longer, as you must be comprehensive and showcase your best work. And what makes it worse is, the candidates could find yourself being rejected without the chance to even confer with the team — how frustrating! Unsurprisingly, I heard from my team’s recruiter some time back that take-home project results in a high drop-off rate within the hiring process (so we removed it). 

But take-home assignments do have value. It tests end-to-end skills from problem framing, coding, writing, to presentation. And the character of working along with your local environment along with your preferred tools now means you may seek AI’s help to finish the project faster and higher! Due to this fact, take-home assignments can easily evolve and turn out to be more common on this recent era, with higher expectations for depth, interpretation, and originality. The challenge, though, is for hiring managers to give you an project that AI cannot easily solve or will only generate the minimum acceptable solution. For instance, an easy data manipulation task is not going to be appropriate, but an open-ended query that requires making assumptions based on domain knowledge, tradeoff discussion, and prioritization will work higher. And a follow-up live interview is at all times helpful to validate the understanding. 

Now let’s summarise the standard interview formats vs. the brand new formats under the AI era:

III. What This Means for Candidates

Above is my tackle how data scientist interview loops will transform under the age of AI. Nonetheless, these shifts should take some time to occur, especially at large firms with a standardized and well-established recruiting process.

So, what should the candidates do to arrange themselves higher ahead of time? 

1. Know when and use AI thoughtfully. As firms start to permit using AI and even evaluate how you employ AI during interviews, understanding use it thoughtfully becomes critical. Don’t just prompt and paste. You need to understand what AI does well and where it falls short, and evaluate the outputs. Not to say that AI can be an excellent helpful tool in interview preparation. It could aid you understand the position higher, arrange a preparation plan, and do mock interviews — I can write an entire article on this (possibly next time). 
2. Understand the business deeply. Now that technical skills are getting easier with AI assistance, business understanding and domain knowledge turn out to be the important thing for a candidate to face out. Due to this fact, everyone should collaborate more with stakeholders at work to develop their business knowledge. And while you prepare for interviews, spend time doing company research to know its product — what can be the important thing metrics, grow the product further with data, and what needs to be the retention strategy. 

Thanks for reading! For those who’re a hiring manager, I’d love to listen to how your team is adapting. And in case you’re a candidate, I hope this helps you prepare smarter for the longer term of interviews.