Rethinking Information Science Interviews within the Age of AI

AI is rewriting the day-to-day of knowledge scientists. , information scientists should discover ways to enhance productiveness and unlock new prospects with AI. In the meantime, this transformation additionally poses a problem to hiring managers: how one can discover the perfect expertise that may thrive within the AI period? One crucial step in constructing a robust AI-empowered information staff is to revamp the hiring course of to raised consider candidates’ means 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 Information Scientist Analytics (DSA) roles, the concepts right here additionally apply to different information positions, reminiscent of Machine Studying Engineers (MLE). 

I. The Conventional Information Scientist Interview Loop

Earlier than speaking about how issues will change, let’s undergo the present construction of knowledge scientist interviews. Apart from 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 fundamental logic.
2. Statistics interviews: Statistics and likelihood questions, in addition to the most typical statistical purposes in information science workflows, reminiscent of A/B testing and causal inference.
3. Machine studying interviews: Deep dive into machine studying algorithms, experiences, and circumstances.
4. Enterprise case interviews: Talk about a hypothetical downside to check analytical considering and enterprise understanding — metrics, funnels, progress, retention methods, and analytical approaches.
5. Behavioral interviews: Commonplace “stroll me via a venture / a time once you XXX” to grasp how candidates deal with particular conditions and if they’re a cultural match. 
6. Cross-functional interviews: Information Scientist is a technical position, however it’s also extremely cross-functional, aiming to drive actual enterprise influence utilizing information. Due to this fact, many information scientist interview loops immediately embody a cross-functional interview spherical to speak with a enterprise accomplice to evaluate the area information, communication expertise, and stakeholder collaboration. 

From the listing above, you may see that information scientist interviews normally have a superb mixture of technical and non-technical evaluations. However with AI getting into the sport, a few of these interviews will change considerably, whereas some will turn into 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 will depend on two issues: 1. Can AI deal with the duty rapidly? 2. Does it inform how the candidate makes use of AI thoughtfully? 

Coding Interviews: Most Prone to Change First

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

In the present day’s coding interviews ask candidates to jot down SQL and Python code accurately. The SQL questions normally require easy joins, CTEs, aggregations, and window capabilities. And the Python questions may very well 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 12 months 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 take a look at. 

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 principally take a look at fundamental syntax, which is perhaps honest for entry-level candidates, however have been failing to guage precise problem-solving for a very long time, even with out the evolution of AI. The truth that AI can reply them rapidly solely makes this spherical much more outdated. 

So, how can we make the coding interviews extra significant? I believe, firstly, we must always permit candidates to make use of AI instruments like GitHub Copilot or Cursor through the coding interview to imitate the brand new work setting with AI. I’ve seen this taking place progressively within the trade. For instance, Canva launched AI-assisted coding interviews not too long ago, and Greenhouse additionally says, “We welcome clear use of generative AI within the interview course of for sure roles with the expectation that candidates can completely clarify the prompts they create and/or focus on in-depth the technical selections they make.” I believe permitting candidates to make use of AI is best than attempting each means to stop them from dishonest with AI, as they’ll use (and are anticipated to make use of) AI at work anyway :). 

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

1. Ideally, we may 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 an alternative 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 primarily based 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 more durable — they must be very acquainted with the datasets and be capable to observe the candidates’ logic, ask follow-up questions, and assess the responses. 
2. Alternatively, we are able to ask candidates to guage the AI outputs — that is most likely simpler to arrange and fewer aggravating and time-consuming than the above format. Whereas AI might help with coding, it’s nonetheless people’ duty to guage the output. Not each AI-generated code is right, 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 determine if the logic is right, if it ignores any edge circumstances, if there’s any higher options, or if the code might be optimized additional — this requires the candidate to completely perceive how one can interprets between the enterprise logic and the code. It’s also simpler to design a regular rubric with this downside setup. 

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

Subsequent, let’s speak about statistics and machine studying interviews. AI is a superb trainer — it explains fundamental stats and machine studying ideas clearly and might help brainstorm completely different methodologies — attempt asking ChatGPT, “clarify p-value to me like I’m 5”. Nonetheless, understanding the theories doesn’t all the time imply making use of the suitable strategies primarily based on enterprise situations. You’ll find a superb instance in my Google Information Science Agent analysis article — it does a fantastic job establishing a modeling framework with purposeful starter code, nevertheless it requires a transparent downside assertion and a clear dataset. Human experience can be mandatory for characteristic engineering, selecting the perfect domain-specific information science practices, and tuning the fashions. Protecting that in thoughts, I believe 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 an alternative of asking remoted questions like “What’s the distinction between Ridge and Lasso Regression?” or “Tips on how to calculate the pattern dimension for an A/B take a look at?”, current a real-world downside and observe how the candidates strategy the questions analytically, if the proposed strategies make sense, and if they impart their concepts logically. It’s not like we not want the candidates to have stable stats and ML information, however we’ll take a look at the information extra seamlessly within the case dialogue. For instance, when going via a hypothetical fraud detection case, we are able to 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 trade. My prediction is that AI will make it much more predominant.  

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

For the remaining two interview varieties, behavioral interviews and cross-functional interviews, they’ll seemingly keep right here. They consider the candidates’ smooth expertise, reminiscent of cross-functional collaboration, communication, battle decision, and possession, in addition to their area information. These are the issues AI can’t exchange. Nonetheless, there may very well be some shifts in what questions individuals ask. Interviewers can add questions in regards to 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’ means to determine AI use circumstances that drive actual enterprise worth.

Take-home Assignments: Nonetheless Controversial, However Helpful

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

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

However take-home assignments do have worth. It exams end-to-end expertise from downside framing, coding, writing, to presentation. And the character of working together with your native setting together with your most well-liked instruments now means you may search AI’s assist to finish the project sooner and higher! Due to this fact, take-home assignments can simply evolve and turn 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 project that AI can’t simply remedy or will solely generate the minimal acceptable resolution. For instance, a easy information manipulation process won’t be applicable, however an open-ended query that requires making assumptions primarily based on area information, tradeoff dialogue, and prioritization will work higher. And a follow-up stay interview is all the time useful to validate the understanding. 

Now let’s summarise the normal interview codecs vs. the brand new codecs beneath the AI period:

III. What This Means for Candidates

Above is my tackle how information scientist interview loops will remodel beneath 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 arrange themselves higher forward of time? 

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

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