Rethinking Data Science Interviews in the Age of AI

AI is rewriting the day-to-day of data scientists. , information scientists should learn to enhance productiveness and unlock new potentialities with AI. In the meantime, this transformation additionally poses a problem to hiring managers: discover the perfect expertise that can thrive within the AI period? One vital step in constructing a powerful AI-empowered information group 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, similar to 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 information scientist interviews. Other than 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 commonest statistical purposes in information science workflows, similar to A/B testing and causal inference.
3. Machine studying interviews: Deep dive into machine studying algorithms, experiences, and circumstances.
4. Enterprise case interviews: Focus on a hypothetical drawback to check analytical pondering and enterprise understanding — metrics, funnels, progress, retention methods, and analytical approaches.
5. Behavioral interviews: Normal “stroll me by a challenge / a time once you XXX” to know 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 affect utilizing information. Subsequently, many information scientist interview loops in the present day embrace 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’ll be able to see that information scientist interviews normally have an excellent 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 vital. 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 vary relies on 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. 

Right now’s coding interviews ask candidates to put in writing SQL and Python code accurately. The SQL questions normally require easy joins, CTEs, aggregations, and window features. And the Python questions may very well be easy information manipulation with pandas and numpy, or simple LeetCode-style questions. However let’s be sincere, these interview questions may be solved by AI simply in the present day. 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 obtained full factors in my take a look at. 

Let’s take one step again. For information scientists, the true coding problem in the present day comes from 1. Understanding the info and finding the proper tables and fields; 2. Translating your information questions into the proper question/code. In different phrases, in the present day’s coding interviews largely take a look at fundamental syntax, which is likely to be truthful 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 shortly solely makes this spherical much more outdated. 

So, how can we make the coding interviews extra significant? I believe, firstly, we must always enable candidates to make use of AI instruments like GitHub Copilot or Cursor throughout the coding interview to imitate the brand new work surroundings with AI. I’ve seen this taking place steadily within the business. For instance, Canva introduced AI-assisted coding interviews just 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 completely clarify the prompts they create and/or talk about in-depth the technical selections they make.” I believe permitting candidates to make use of AI is best than attempting each means to forestall them from dishonest with AI, as they are going to 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 surroundings with a number of documented tables and ask the candidates to do a dwell 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 is not going to 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 software, how do they immediate, iterate, and consider the output. Although this does make interviewers’ lives tougher — they should be very conversant in the datasets and have the ability to comply with 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 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’ accountability 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 establish if the logic is right, if it ignores any edge circumstances, if there’s any higher options, or if the code may be optimized additional — this requires the candidate to completely perceive interprets between the enterprise logic and the code. Additionally it is simpler to design a regular rubric with this drawback setup. 

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

Subsequent, let’s discuss statistics and machine studying interviews. AI is a good instructor — it explains fundamental stats and machine studying ideas clearly and will help brainstorm totally different methodologies — attempt asking ChatGPT, “clarify p-value to me like I’m 5”. Nevertheless, figuring out the theories doesn’t at all times imply making use of the suitable strategies based mostly on enterprise situations. You could find an excellent instance in my Google Data Science Agent evaluation article — it does a fantastic 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 vital for characteristic engineering, selecting the perfect domain-specific information science practices, and tuning the fashions. Maintaining 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 “Methods to calculate the pattern dimension for an A/B take a look at?”, 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 are going to take a look at the information extra seamlessly within the case dialogue. For instance, when going by 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 business. 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 are going to probably keep right here. They consider the candidates’ mushy expertise, similar to cross-functional collaboration, communication, battle decision, and possession, in addition to their area information. These are the issues AI can not substitute. Nevertheless, 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 establish AI use circumstances that drive actual enterprise worth.

Take-home Assignments: Nonetheless Controversial, However Helpful

In addition to these frequent interview codecs, there’s additionally a controversial one which comes up in information science interview loops every now and then — 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. Generally there are guiding questions, typically not. Deliverables vary from a Jupyter pocket book to a cultured slide deck. 

I do know there are candidates who actually hate it. It takes numerous effort — although recruiters at all times say common candidates take about 4 hours, the precise time you spend is normally considerably longer, as you wish 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 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 surroundings along with your most popular instruments now means you’ll be able to 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 larger expectations for depth, interpretation, and originality. The problem, although, is for hiring managers to give you an task that AI can not simply remedy or will solely generate the minimal acceptable answer. For instance, a easy information manipulation activity is not going to be acceptable, 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 dwell interview is at all times useful to validate the understanding. 

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

III. What This Means for Candidates

Above is my tackle how information scientist interview loops will remodel underneath the age of AI. Nevertheless, these shifts should take some time to occur, particularly at massive corporations 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 use AI thoughtfully. As corporations begin to enable the usage of AI and even consider how you utilize AI throughout interviews, understanding use it thoughtfully turns into vital. Don’t simply immediate and paste. You need to perceive what AI does effectively and the place it falls quick, and consider the outputs. To not point out that AI can also be an excellent useful software in interview preparation. It may show you how to perceive the place higher, arrange a preparation plan, and do mock interviews — I can write an entire 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 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 once you put together for interviews, spend time doing firm analysis to know its product — what can be the important thing metrics, develop the product additional with information, and what needs to be the retention technique. 

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