Instacart Data Scientist Interview Guide

Instacart data scientist interviews, as candidates describe them, lean toward experimentation and causal inference in a marketplace setting. One candidate was asked how to measure an ultra-fast express delivery rollout in a single city. The discussion narrowed quickly into which formula to use and which variables to focus on. The same candidate reported questions on diff-in-diff, synthetic control and establishing causality from observational data. A senior candidate was asked how to assess a reduced delivery fee in one market. That candidate said they leaned too heavily on a randomized trial when observational methods were the better fit, and they were rejected after that first round.

Several candidates describe a technical screen on SQL and statistics with an A/B testing emphasis, followed by a take-home or case study and a panel or virtual onsite. Reported onsite pieces include:

• a case-study review of a feature rollout with two test groups and a control;
• a product question about KPIs moving in opposite directions;
• a rapid-fire statistics round covering hypothesis testing and the central limit theorem.

Reports differ by candidate and level, so the exact loop may vary.

SQL can take an unusual form. One candidate was shown a ChatGPT-generated query and asked to find what was wrong with it, so practice reading and critiquing queries as well as writing them. Another candidate described a design discussion on a personalization algorithm for product recommendations. It covered feature engineering and model choices and was followed by a behavioral round.

One candidate described a process of a little over two months that ended in a hiring pause. Practice A/B testing, causal methods and clear presentation of recommendations, and keep other options active while you wait.