Rethinking Data Science Interviews in the Age of AI

AI is rewriting the day-to-day of data scientists. , information scientists should discover ways to enhance productiveness and unlock new potentialities with AI. In the meantime, this transformation additionally poses a problem to hiring managers: the best way to discover the very best expertise that may thrive within the AI period? One essential step in constructing a powerful AI-empowered information group is to revamp the hiring course of to raised consider candidates’ potential to work alongside AI. 

On this article, I’ll share my perspective on how information scientist interviews ought to (would) evolve within the age of AI. Whereas my focus right here is on Knowledge Scientist Analytics (DSA) roles, the concepts right here additionally apply to different information positions, resembling Machine Studying Engineers (MLE). 

I. The Conventional Knowledge Scientist Interview Loop

Earlier than speaking about how issues will change, let’s undergo the present construction of information scientist interviews. Except for the preliminary recruiter name and hiring supervisor screening, a typical information scientist interview course of contains:

1. Coding interviews: SQL or Python coding questions to check syntax and primary logic.
2. Statistics interviews: Statistics and chance questions, in addition to the most typical statistical functions in information science workflows, resembling A/B testing and causal inference.
3. Machine studying interviews: Deep dive into machine studying algorithms, experiences, and instances.
4. Enterprise case interviews: Talk about a hypothetical drawback to check analytical considering and enterprise understanding — metrics, funnels, progress, retention methods, and analytical approaches.
5. Behavioral interviews: Normal “stroll me by way of a challenge / a time while you XXX” to know how candidates deal with particular conditions and if they’re a cultural match. 
6. Cross-functional interviews: Knowledge Scientist is a technical position, however it is usually extremely cross-functional, aiming to drive actual enterprise affect utilizing information. Subsequently, many information scientist interview loops immediately embody a cross-functional interview spherical to speak with a enterprise associate to evaluate the area information, communication expertise, and stakeholder collaboration. 

From the listing above, you possibly can see that information scientist interviews often have mixture of technical and non-technical evaluations. However with AI coming into the sport, a few of these interviews will change considerably, whereas some will develop into much more essential. Let’s break it down.

II. How Interviews Will Shift within the Age of AI

For my part, how the interview loops are going to alter is determined by two issues: 1. Can AI deal with the duty shortly? 2. Does it inform how the candidate makes use of AI thoughtfully? 

Coding Interviews: Most Prone to Change First

What can AI do shortly? Easy coding duties. Subsequently, the coding interview might be the primary one to be impacted. 

At this time’s coding interviews ask candidates to jot down SQL and Python code appropriately. The SQL questions often require easy joins, CTEs, aggregations, and window capabilities. And the Python questions could possibly be simple information manipulation with pandas and numpy, or simple LeetCode-style questions. However let’s be trustworthy, these interview questions might be solved by AI simply immediately. In my article one yr in the past, I evaluated how ChatGPT, Claude, and Gemini carry out in easy SQL duties, and was impressed already by all three — Claude 3.5 Sonnet even bought full factors in my check. 

Let’s take one step again. For information scientists, the true coding problem immediately comes from 1. Understanding the information and finding the proper tables and fields; 2. Translating your information questions into the proper question/code. In different phrases, immediately’s coding interviews largely check primary syntax, which is likely to be honest for entry-level candidates, however have been failing to judge precise problem-solving for a very long time, even with out the evolution of AI. The truth that AI can reply them shortly solely makes this spherical much more outdated. 

So, how can we make the coding interviews extra significant? I feel, firstly, we should always enable candidates to make use of AI instruments like GitHub Copilot or Cursor throughout the coding interview to imitate the brand new work setting with AI. I’ve seen this taking place regularly within the business. For instance, Canva introduced AI-assisted coding interviews lately, and Greenhouse also says, “We welcome clear use of generative AI within the interview course of for sure roles with the expectation that candidates can totally clarify the prompts they create and/or focus on in-depth the technical selections they make.” I feel permitting candidates to make use of AI is healthier than attempting each means to forestall them from dishonest with AI, as they may use (and are anticipated to make use of) AI at work anyway :). 

In the meantime, as a substitute of asking easy SQL/Python questions, I’ve a few concepts:

1. Ideally, we might arrange an setting with a number of documented tables and ask the candidates to do a stay problem-solving session with the assistance of AI. As a substitute of asking questions like “write a question to calculate MAU since 2024”, ask extra open-ended questions like “how would you examine buyer churn since 2024?”. The analysis won’t solely be based mostly on code accuracy, but in addition on how the candidates body their evaluation and interpret the outcomes. And when the candidate interacts with the AI instrument, how do they immediate, iterate, and consider the output. Although this does make interviewers’ lives tougher — they should be very accustomed to the datasets and be capable of observe the candidates’ logic, ask follow-up questions, and assess the responses. 
2. Alternatively, we will ask candidates to judge the AI outputs — that is in all probability simpler to arrange and fewer worrying and time-consuming than the above format. Whereas AI will help with coding, it’s nonetheless people’ duty to judge the output. Not each AI-generated code is appropriate, even when it runs with out errors. The interviewer can describe what they’re attempting to do and present AI-generated code, then ask the candidates to establish if the logic is appropriate, if it ignores any edge instances, if there may be any higher options, or if the code might be optimized additional — this requires the candidate to totally perceive the best way to interprets between the enterprise logic and the code. It is usually simpler to design a typical rubric with this drawback setup. 

Statistics and Machine Studying Interviews: Much less Principle, Extra Context

Subsequent, let’s speak about statistics and machine studying interviews. AI is a good instructor — it explains primary stats and machine studying ideas clearly and will help brainstorm totally different methodologies — strive asking ChatGPT, “clarify p-value to me like I’m 5”. Nonetheless, understanding the theories doesn’t at all times imply making use of the suitable strategies based mostly on enterprise situations. You could find instance in my Google Data Science Agent evaluation article — it does an important job establishing a modeling framework with useful starter code, but it surely requires a transparent drawback assertion and a clear dataset. Human experience can also be obligatory for function engineering, selecting the very best domain-specific information science practices, and tuning the fashions. Conserving that in thoughts, I feel statistics and machine studying interviews ought to ask fewer theoretical questions or coding fashions from scratch, however combine extra with enterprise case interviews to check if the candidates can apply theories to a enterprise context. So as a substitute of asking remoted questions like “What’s the distinction between Ridge and Lasso Regression?” or “Easy methods to calculate the pattern dimension for an A/B check?”, current a real-world drawback and observe how the candidates strategy the questions analytically, if the proposed strategies make sense, and if they convey their concepts logically. It’s not like we not want the candidates to have stable stats and ML information, however we’ll check the information extra seamlessly within the case dialogue. For instance, when going by way of a hypothetical fraud detection case, we will ask why the candidate proposes XGBoost over Random Forest, and whether it is higher to impute lacking values in family revenue because the median or zero.  

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

Behavioral & Cross-functional Interviews: Largely Unchanged, However With New Twists

For the remaining two interview sorts, behavioral interviews and cross-functional interviews, they may doubtless keep right here. They consider the candidates’ mushy expertise, resembling cross-functional collaboration, communication, battle decision, and possession, in addition to their area information. These are the issues AI can not change. Nonetheless, there could possibly be some shifts in what questions folks ask. Interviewers can add questions concerning the candidates’ previous expertise with AI instruments to get extra sign on how they use AI to spice up productiveness and remedy issues. For instance, a product supervisor may ask, “How can we use AI to enhance buyer onboarding?” These conversations can floor the candidates’ potential to establish AI use instances that drive actual enterprise worth.

Take-home Assignments: Nonetheless Controversial, However Helpful

Moreover these frequent interview codecs, there may be additionally a controversial one which comes up in information science interview loops every so often — Take-home assignments. It’s often within the format of offering a dataset and asking the candidates to do an evaluation or construct a mannequin. Generally there are guiding questions, typically not. Deliverables vary from a Jupyter pocket book to a elegant slide deck. 

I do know there are candidates who actually hate it. It takes a number of effort — although recruiters at all times say common candidates take about 4 hours, the precise time you spend is often considerably longer, as you need to be complete and showcase your greatest work. And what makes it worse is, the candidates could find yourself being rejected with out the chance to even discuss to the group — how irritating! Unsurprisingly, I heard from my group’s recruiter some time again that take-home task results in a excessive drop-off price within the hiring course of (so we eliminated it). 

However take-home assignments do have worth. It assessments end-to-end expertise from drawback framing, coding, writing, to presentation. And the character of working along with your native setting along with your most popular instruments now means you possibly can search AI’s assist to finish the task sooner and higher! Subsequently, take-home assignments can simply evolve and develop into extra frequent on this new period, with greater expectations for depth, interpretation, and originality. The problem, although, is for hiring managers to provide you with an task that AI can not simply remedy or will solely generate the minimal acceptable resolution. For instance, a easy information manipulation activity won’t be applicable, however an open-ended query that requires making assumptions based mostly on area information, tradeoff dialogue, and prioritization will work higher. And a follow-up stay interview is at all times useful to validate the understanding. 

Now let’s summarise the standard interview codecs vs. the brand new codecs below the AI period:

III. What This Means for Candidates

Above is my tackle how information scientist interview loops will rework below the age of AI. Nonetheless, these shifts should take some time to occur, particularly at giant firms with a standardized and well-established recruiting course of.

So, what ought to the candidates do to organize themselves higher forward of time? 

1. Know when and the best way to use AI thoughtfully. As firms begin to enable using AI and even consider how you utilize AI throughout interviews, understanding the best way to use it thoughtfully turns into essential. Don’t simply immediate and paste. You must perceive what AI does nicely and the place it falls quick, and the best way to consider the outputs. To not point out that AI can also be an excellent useful instrument in interview preparation. It will possibly show you how to perceive the place higher, arrange a preparation plan, and do mock interviews — I can write a complete article on this (possibly subsequent time). 
2. Perceive the enterprise deeply. Now that technical expertise are getting simpler with AI help, enterprise understanding and area information develop into the important thing for a candidate to face out. Subsequently, everybody ought to collaborate extra with stakeholders at work to develop their enterprise information. And while you put together for interviews, spend time doing firm analysis to know its product — what can be the important thing metrics, the best way to develop the product additional with information, and what ought to be the retention technique. 

Thanks for studying! If you happen to’re a hiring supervisor, I’d love to listen to how your group is adapting. And in the event you’re a candidate, I hope this helps you put together smarter for the way forward for interviews.