Went through the MongoDB DS loop earlier this year for a role on their Atlas product analytics team. The process was different enough from typical DS interviews that I want to write it up properly.
Recruiter screen: Standard 30 minutes. They asked about my experience with product analytics (not research, specifically product), my SQL comfort level, and one question about a metric I'd defined or owned end to end. Know what metrics you actually care about and why.
Take-home: They sent a dataset, 72 hours. You had to produce a short analysis answering a business question about funnel drop-off. Expectations felt like "thoughtful senior analyst" level, not research paper level. Python or R, submit a Jupyter notebook. They wanted clean code and clear communication, not fancy models.
Technical screen (SQL + stats): 45 minutes, live. SQL was legit hard. Window functions, CTEs, multi-step aggregations. One question had a self-join that I fumbled and had to back up and redo. Stats questions were mostly conceptual: explain p-values without jargon, how would you detect selection bias in an A/B test, what's the difference between correlation and causation in a product context. No probability brainteasers, thankfully.
Case interview: A hypothetical. "Atlas free tier activation rate is declining month over month. Walk me through how you'd diagnose this." This is really a structured thinking test. Start with clarifying questions (is this absolute or relative, is it a data pipeline issue, is it a product change, is it external), then systematically segment and hypothesize. They're checking if you can tell the difference between a real product problem and a data artifact.
Final behavioral round: Standard STAR, focused on stakeholder communication and technical influence. One question about explaining a counterintuitive result to a non-technical exec.
Overall: MongoDB DS interviews lean more BI/analytics than modeling. If you're a deep ML engineer expecting to talk about model architecture, reframe your expectations. If you're a strong SQL analyst with solid product sense and some stats chops, you'll feel at home.
Comp for the offer (senior DS, NYC, 2026): base around $165k, bonus target 15%, RSUs vesting over 4 years. Total first-year around $210k depending on RSU grant size.