Apple Data Scientist Interview Loop
Apple hires data scientists team by team, and the loop depends on the hiring manager. The usual shape is a recruiter call, a conversation with the hiring manager about your resume, and one or two coding screens on SQL and Python. Then a virtual onsite of three to eight interviews. Recruiters often do not describe the loop in advance, so ask. Some teams add a take-home with a presentation, and the teams that run experiments ask about them in depth.
What decides it
Fit with the team and clear explanation. The hiring manager round cuts many candidates before any code is written. Know your own projects well: the business problem, the model, the metric, and what you told stakeholders.
Practice by round
SQL and pandas basics. Apple's screens are practical. Join two tables, remove duplicates, count the right thing. There is no autocomplete and little time, so speed on simple queries matters.
Two harder SQL questions, and Python that works with strings, lists and sliding windows. The screen sometimes ends with a scenario question, so be ready to switch from code to a plan.
| Question | Difficulty |
|---|---|
| Users Exclusive Per Client | Medium |
| Bottom 2 Companies By Mobile Usage | Medium |
| Frequent Customers | Medium |
| Customers Without Orders | Medium |
| Department Salaries | Medium |
| Number of Speakers By Language | Medium |
| MacBook Pro Events | Medium |
| Apple Product Counts | Medium |
| New And Existing Users | Hard |
| Most Popular Client For Calls | Hard |
Product cases on Apple's own kind of questions: did the feature launch work, testing a ranking change, engagement that dropped after an OS update, which subscription metric to pick, and prioritizing between two features. Then the mini-analyses, which are the same work on a dataset: trial conversions across devices and active subscribers by month.
| Case study | Difficulty |
|---|---|
| Did the Feature Launch Work? | Medium |
| Testing a Ranking Change | Medium |
| Engagement Dropped After OS Update | Medium |
| Which Subscription Metric | Medium |
| Prioritizing Between Two Features | Medium |
| Data project | Difficulty |
|---|---|
| Trial Conversions Across Devices | Medium |
| Active Subscribers by Month | Medium |
Regression and what to do with a model you are asked to critique, probability puzzles like defective bulbs, and experiment questions like one winner out of twenty. Some teams go deeper into the mathematics than candidates expect.
| Question | Difficulty |
|---|---|
| Users in Meaningful Segments | Medium |
| Bounding Box Regressor | Medium |
| Cosine Convolution | Medium |
| Defective Bulbs | Medium |
| One Winner Out of Twenty | Medium |