Rethinking Knowledge Science Interviews within the Age of AI

AI is rewriting the day-to-day of information 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 way to discover the most effective expertise that may thrive within the AI period? One essential step in constructing a robust AI-empowered information workforce is to revamp the hiring course of to raised consider candidates’ capability 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, equivalent to 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. 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 primary logic.
2. Statistics interviews: Statistics and likelihood questions, in addition to the most typical statistical purposes in information science workflows, equivalent to A/B testing and causal inference.
3. Machine studying interviews: Deep dive into machine studying algorithms, experiences, and instances.
4. Enterprise case interviews: Focus on a hypothetical drawback to check analytical considering and enterprise understanding — metrics, funnels, development, retention methods, and analytical approaches.
5. Behavioral interviews: Commonplace “stroll me by way of a challenge / a time if you XXX” to grasp how candidates deal with particular conditions and if they’re a cultural match. 
6. Cross-functional interviews: Knowledge Scientist is a technical position, however additionally it is extremely cross-functional, aiming to drive actual enterprise influence utilizing information. Due to this fact, many information scientist interview loops right now embody a cross-functional interview spherical to speak with a enterprise accomplice to evaluate the area data, communication abilities, and stakeholder collaboration. 

From the listing above, you possibly can see that information scientist interviews normally have a great 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 grow to be much more necessary. 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 depends upon 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. 

Immediately’s coding interviews ask candidates to write down SQL and Python code appropriately. The SQL questions normally require easy joins, CTEs, aggregations, and window features. 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 could be solved by AI simply right now. 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 received full factors in my take a look at. 

Let’s take one step again. For information scientists, the true coding problem right now comes from 1. Understanding the info and finding the right tables and fields; 2. Translating your information questions into the right question/code. In different phrases, right now’s coding interviews largely take a look at primary syntax, which is perhaps 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 rapidly solely makes this spherical much more outdated. 

So, how can we make the coding interviews extra significant? I feel, firstly, we should always permit 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 progressively within the trade. For instance, Canva launched AI-assisted coding interviews lately, 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 totally clarify the prompts they create and/or focus on in-depth the technical choices they make.” I feel permitting candidates to make use of AI is best than attempting each means to stop 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 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 reside 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 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 device, how do they immediate, iterate, and consider the output. Although this does make interviewers’ lives more durable — they should be very acquainted with the datasets and have the ability 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 in all probability simpler to arrange and fewer disturbing and time-consuming than the above format. Whereas AI may 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 instances, if there may be any higher alternate options, or if the code could be optimized additional — this requires the candidate to completely perceive the way to interprets between the enterprise logic and the code. Additionally it is simpler to design a normal rubric with this drawback setup. 

Statistics and Machine Studying Interviews: Much less Idea, 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 may help brainstorm completely different methodologies — attempt 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 primarily based on enterprise situations. You will discover a great instance in my Google Knowledge Science Agent analysis article — it does a fantastic job organising a modeling framework with practical starter code, nevertheless it requires a transparent drawback assertion and a clear dataset. Human experience can be crucial for function engineering, selecting the most effective domain-specific information science practices, and tuning the fashions. Maintaining 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 “The best way to calculate the pattern measurement 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 impart their concepts logically. It’s not like we not want the candidates to have strong stats and ML data, however we’ll take a look at the data extra seamlessly within the case dialogue. For instance, when going by way of 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 earnings 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 sorts, behavioral interviews and cross-functional interviews, they are going to possible keep right here. They consider the candidates’ comfortable abilities, equivalent to cross-functional collaboration, communication, battle decision, and possession, in addition to their area data. These are the issues AI can not exchange. Nonetheless, there could possibly be some shifts in what questions individuals 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’ capability 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 sometimes — 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 elegant slide deck. 

I do know there are candidates who actually hate it. It takes quite a lot of 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 may find yourself being rejected with out the chance to even speak to the workforce — how irritating! Unsurprisingly, I heard from my workforce’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 assessments end-to-end abilities 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 project sooner and higher! Due to this fact, take-home assignments can simply evolve and grow to be extra frequent on this new period, with larger expectations for depth, interpretation, and originality. The problem, although, is for hiring managers to provide you with an project that AI can not 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 data, tradeoff dialogue, and prioritization will work higher. And a follow-up reside interview is at all times useful to validate the understanding. 

Now let’s summarise the standard 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 rework underneath the age of AI. Nonetheless, these shifts should take some time to occur, particularly at giant 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 the way to use AI thoughtfully. As corporations begin to permit using AI and even consider how you employ AI throughout interviews, understanding the way to use it thoughtfully turns into essential. Don’t simply immediate and paste. You need to perceive what AI does properly and the place it falls quick, and the way to consider the outputs. To not point out that AI can be a brilliant useful device in interview preparation. It might probably assist you perceive the place higher, arrange a preparation plan, and do mock interviews — I can write an entire article on this (possibly subsequent time). 
2. Perceive the enterprise deeply. Now that technical abilities are getting simpler with AI help, enterprise understanding and area data grow to be 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 data. And if you put together for interviews, spend time doing firm analysis to grasp its product — what can be the important thing metrics, the way to develop the product additional with information, and what ought to be the retention technique. 

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