Apple Data Scientist interview questions

32 Data Scientists have shared notes from their Apple interviews, with the questions they were asked in each round. That is 9% of the 372 Apple interview notes on Screna AI.

32 Data Scientist interview notes

Apple·Data Scientist·Online Assessment (OA)

Jul 2026
Apple DS interview that was basically three coding tasks back to back, SQL and Python both fair game. Nothing too wild but the third problem had me second-guessing my greedy approach the whole time.
  • Given an employees table with salaries, return the second highest distinct salary, or NULL if one doesn't exist.
  • You have a users table and a user_events table. Merge them so each event row includes user attributes, keeping only events with a matching user.
  • Given a list of employees with salaries and a total budget B, find the maximum number of employees you can hire without exceeding the budget.

“Pretty classic, but the NULL edge case is where people trip up.”

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Apple·Data Scientist·Technical Phone Screen

Jul 2026
Apple Data Scientist technical screen, one coding question centered on sliding window stuff. Pretty straightforward session, felt more like a warm-up than a deep technical grilling.
  • You have an array of positive integers where each value represents website hits in a given minute. Given a window size k, find the maximum total hits across any contiguous window of length k. What's the time and space complexity of your solution?

“Knew the sliding window trick going in, so I coded it up pretty fast.”

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Apple·Data Scientist·Technical Phone Screen

Jul 2026
Apple data scientist interview, technical phone screen focused on ML pipelines. One big open-ended question about building a churn model end-to-end in scikit-learn. Felt more like a design conversation than a coding test, which I wasn't fully prepared for.
  • Walk through how you'd build a churn-prediction pipeline from scratch in scikit-learn, covering preprocessing, model selection, hyperparameter tuning, and packaging it for production.

“This was basically the whole interview.”

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Apple·Data Scientist·Recruiter / HR Screen

Jul 2026
Phone behavioral screen for a Data Scientist role at Apple. Pretty short call, just one question but it required some real prep to not sound generic.
  • Why do you want to work at Apple, and which of Apple's values connect with your past work?

“I had a decent answer prepared but partway through I realized I was just listing values without actually tying them to anything real.”

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Apple·Data Scientist·Technical Phone Screen

Jul 2026
Apple DS interview with a product analytics case around a checkout conversion drop. The question was meaty and covered a lot of ground, felt like they wanted to see if you'd actually think through the noise vs. signal problem before jumping to conclusions.
  • A new feature was released and checkout conversion appears to have dropped. How would you determine whether this is a genuine regression or just random noise? Walk through the metrics, segmentation approaches, and any experimental or quasi-experimental methods you'd apply.

“I started with the time-series angle, basically asking whether the drop is outside normal variance before doing anything else.”

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Apple·Data Scientist·Technical Phone Screen

Jul 2026
Apple data scientist technical screen, one question, all SQL. They handed me a sessionization problem with a strict set of requirements and just watched me work through it.
  • Write a single ANSI-SQL query that assigns per-user session IDs when the gap between consecutive events exceeds 30 minutes, then computes session_start, session_end, event_count, session_length_seconds, and next_session_gap_seconds using window functions including LEAD.

“This is the kind of question that looks manageable until you're actually writing it live.”

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Apple·Data Scientist·Technical Phone Screen

Jul 2026
Phone screen for a Data Scientist role at Apple, one algorithm question the whole time. The problem wasn't obscure but the space constraint made it genuinely tricky and I wasn't fully prepared for the follow-up on complexity trade-offs.
  • Write a function that finds the smallest missing positive integer in an unsorted array, with O(n) time and O(1) space. Then walk through the complexity trade-offs of your approach.

“I knew the naive version instantly, sort the array and scan.”

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Apple·Data Scientist·Technical Phone Screen

Jun 2026
Growth analytics interview at Meta focused on Instagram monetization. The whole thing revolved around one big multi-part case: funnel analysis, experiment ideation, and metric trade-off reasoning. Pretty demanding for a single question but it covered a lot of ground.
  • If the goal is to increase purchase volume on Instagram, what data would you look at first and why, and what product or experiment ideas would you propose to actually move that number?
  • After launching a feature meant to boost purchases, you notice a separate key metric has declined. How do you figure out what's going on and decide whether to roll back, keep going, or iterate?

“I started with the funnel, which felt right, but I spent too long on the top of it and the interviewer kept nudging me toward checkout drop-off specifically.”

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Apple·Data Scientist·Technical Phone Screen

Jun 2026
Stats fundamentals check for a Data Scientist role at Apple. Pretty standard stuff but the multi-part format meant you had to keep your definitions tight and your examples on point, no room to ramble.
  • What is the difference between a p-value and a confidence interval, when would you choose a t-test over a chi-square test, and how would you define Type I and Type II errors with examples?

“Three questions bundled into one and I kind of tripped over myself trying to answer them in order.”

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Apple·Data Scientist·Take-home Assignment

Jun 2026
Apple data scientist take-home that was basically a full ML system design exercise disguised as a coding prompt. One question, but it covered everything from feature engineering to post-deployment monitoring, so budget your time accordingly.
  • Build an end-to-end scikit-learn pipeline to predict 28-day churn using only features available at decision time, handling high-cardinality categoricals, class imbalance, temporal leakage, hyperparameter tuning, probability calibration, and post-deployment drift monitoring.

“This is a lot to fit into one question.”

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