Went through Instacart's DS interview loop in March 2026 for a mid-senior DS role on their growth team. Want to be specific because most posts about DS interviews are either too vague or 3 years out of date.
Loop structure: SQL/coding screen (45 min, done async or live, mine was live) Case study / product analytics (60 min) Stats / probability (45 min) Behavioral (45 min)
SQL round: Three questions on a realistic-looking e-commerce schema (orders, order_items, users, shoppers). Difficulty: the first was a join + aggregate, something like compute average order value per user segment over the last 30 days. The second was a window function question: rank shoppers by fulfillment rate within each market. The third was trickier and involved a self-join to find users who placed two orders within 7 days of each other (retention signal). Know your window functions and self-joins cold.
Case study: They gave me a scenario: shopper pickup time has increased by 12% in the Southeast region. Diagnose it and recommend next steps. I walked through: is it a data issue, is it all categories or specific ones, is it new shoppers or experienced ones, did something change in store layouts or partner relationships. Then recommended a small experiment: A/B a guided picking route feature with experienced shoppers in one market. They asked for metrics, guardrails, and rough sample size. Spend time on metrics.
Stats round: Not super theoretical but not trivial either. Questions included: explain the difference between precision and recall and when you'd optimize for each. You're running an A/B test and your primary metric improved but a guardrail metric degraded. What do you do. Explain how you'd build a propensity model to identify shoppers at risk of churning. That last one got into feature selection and calibration.
Overall: Hard but fair. Takes about 3 weeks start to finish.