Did the Notion DS interview loop in February 2026. Posting the breakdown because the mix of SQL, case, and stats was different from what I'd seen at other PLG-heavy SaaS companies.
Background: I'm a mid-level DS with 5 years, mostly growth and product analytics. Applied for a product DS role on their growth team.
Structure. Recruiter screen, take-home, then three back-to-back virtual rounds: SQL/technical, product case, and behavioral.
Take-home. ~3 hours. Given a fictional dataset and asked to answer a set of product questions. Mixture of SQL queries and a short written section interpreting the results. They explicitly said they'd evaluate the quality of my communication, not just whether the numbers were right. Write clean, readable SQL and write good prose around your findings. Don't just throw a wall of numbers at them.
SQL round. Live, 45 minutes. Two or three problems. One was a retention calculation: given a table of user events, write a query to compute N-day retention for each cohort. This is bread and butter DS SQL. Know your window functions cold. The second question was about aggregating funnel data across multiple event types. Not hard, but sloppy SQL will cost you.
Product case round. Most interesting round. They gave me a scenario: engagement with a specific Notion feature has dropped 15% over six weeks. Walk me through how you'd diagnose this. Standard product analytics structure works: segment the drop, rule out instrumentation issues, look for external causes, then build hypotheses by feature/cohort/surface. They pushed on whether I'd distinguish a detection problem from a real behavior change. That distinction mattered.
Stats questions. Light to moderate. A/B testing setup and interpretation, basic p-value / type I vs. type II error, and one question about what to do when a test shows a statistically significant but small effect. That last one is a judgment call question. Know when statistical significance isn't business significance.
Overall: weighted toward SQL and product judgment over machine learning. If you're a heavy ML DS looking for an applied modeling role this might not be the loop you expected.