Big Tech Data Scientist Interviews are Becoming AI Native

For the last several years, the format and topics covered in data scientist technical interviews at big tech companies have remained rather consistent. Technical interview rounds typically assess one’s SQL/Python competency, core statistics/probability knowledge, A/B testing concepts, and ML/causal inference fundamentals. These skills tend to be staple for the day-to-day job and are therefore appropriately assessed often.

However, with the recent rise of AI coding tools and their growing usage within big tech companies, the data scientist technical screens are quickly evolving to reflect the change of everyday data scientist work as enabled by AI. While SQL/Python questions aren’t going away, the way that these skills are assessed in interviews is changing significantly.

Across the industry, companies are evolving their technical screens to assess a candidate’s AI competency across two verticals:

1. The ability to answer analytical questions quickly with the aid of AI.
2. The ability to review and resolve flaws in AI generated output.

Skill #1: Ability to answer analytical questions quickly with the aid of AI

AI is expected to be one of the largest productivity enablers of our time and several major tech firms have already shifted their internal tooling to reflect this new reality.

At Meta, 77% of Data Scientists use an internally developed tool called "Analytics Agent" to automate analyses. Instead of writing custom SQL for every request, data scientists use natural language prompts to let the agent discover relevant tables, write code, run queries, and produce insights. A similar trend is unfolding at Google with the launch of the Data Science Agent in Colab, which allows data scientists to describe their analytical task and have the agent complete it on their behalf.

The silver lining across across both of these examples is that the act of writing analytics code is quickly becoming a commodity and the real value is in being able to leverage these tools efficiently to answer questions that will bring value to the business.

The most specific way data scientist interviews are being altered to reflect this change is that SQL/Python coding assessments are now coupled with an available AI agent that can be used to help generate code to answer the question. With coding interviews now featuring an AI companion, the nature of interview questions are changing in two major ways:

• Interview questions are redesigned so that they can’t be “one-shotted” with AI and require specific human-driven decision points to arrive at a conclusion.
  • Ex: an interview question about building a spam classifier may ask you to select a classification threshold for considering a message to be spam. Answering such a question requires both understanding risk tolerance and downstream product actions that will be taken, which requires human judgement / decision-making.
• Simply producing a correct solution with AI is insufficient to receive credit. Candidates are asked to explain the logic behind the code that the AI generated.
  • Ex: simply prompting AI to generate the SQL code to answer a question about pulling data from a database won’t be counted as “success” unless you can also explain the logic that the AI generated and whether there are any changes that you would make.

An example of this in practice, is the recent shift in Meta’s technical DS screen, where candidates are encouraged to use an LLM interface in CoderPad to generate initial query structures, but are then judged on their ability to pivot the analysis when the interviewer introduces a "data surprise"—a sudden change in the underlying schema or a business constraint that the AI cannot possibly know.

Skill #2: The ability to review and resolve flaws in AI generated output

While AI is enabling data scientists to arrive at answers to analytical questions at a much faster pace, this speed-up comes with significant risk of mistakes when AI hallucinates. While hallucinations are a known issue across all uses of AI, they are especially critical in data science work where one small issue/assumption in a query can greatly sway the results of an entire analysis.

For example, multiple law firms and attorneys have filed court documents containing cases that never existed because ChatGPT (or similar models) invented them. Courts have sanctioned attorneys and required explanations when these hallucinated citations were discovered. The issue wasn't that the AI generated text—it generated plausible-looking evidence that users failed to verify.

Furthermore, in the past, we’ve observed examples of data scientists incorrectly leveraging ML models backfire such as in the infamous collapse of Zillow Offers, Zillow’s short-lived house-flipping program. In 2021, Zillow heavily relied on models—including Prophet—to forecast housing prices, overpaid for inventory, and ultimately suffered billions in losses that forced the program's shutdown

Both of these examples highlight that while AI is quickly being embedded within data scientist workflows, there is a great responsibility that still resides with the data scientist to review the output and ensure its accuracy. Interviews are now changing to evaluate whether candidate can do this succesfully.

An example of this in practice is with Roblox’s initial technical data scientist interview screen where the candidate is provided a question/scenario and an AI-generated solution, and is asked to go through the produced solution for any major flaws/incorrect assumptions. The interviewer is gauging whether the candidate understands the fundamentals to be able to review the AI-generated results and ensure the final solution is accurate.

What can you do to prepare for AI components of the data scientist interview?

Preparing for data scientist interviews in the new AI-era requires shifting from rote memorization of theory to understanding fundamentals and practicing implementing these with the help of AI. Below are four strategies that can help you accomplish this:

Closing Remarks

The data scientist role is not being replaced by AI; it is being "leveled up." The technical bar is shifting from the ability to manually write perfect syntax to the ability to drive 10x insights. In this new landscape, the most successful candidates will be those who treat AI as a high-powered intern—someone who can do the heavy lifting, but still requires a senior "manager" to provide the strategic direction, the final verification, and the ultimate business context. The data science interviews of the future aren’t about whether you can code, but rather whether you can lead an AI to the right answer much faster than you could write up the solution yourself.