Did the Lyft DS loop for a mid-level role (their equivalent of L4) in February. Going to break this down because the DS loop is structured differently from the SWE loop and I couldn't find a clean writeup when I was prepping.
Loop structure for DS: SQL round (45 min) Product/case round (45 min) Stats/probability round (45 min) Behavioral (45 min) HM intro (30 min)
No coding (Python/ML) for this particular role. Other DS roles at Lyft that are more ML-adjacent do include a modeling round.
SQL round: This is legitimately hard. Not 'write a SELECT with a JOIN' hard. The problem I got involved a multi-step window function with a self-join to compute some driver-level metric over a rolling window. Think: for each driver, compute their 7-day rolling completion rate, then identify which drivers dropped below a threshold after a specific event.
They give you a schema upfront and 5 minutes to read it. The data model involves rides, drivers, timestamps, statuses. Lyft-domain-specific but interpretable. You're in a shared SQL editor.
Tip: verbalize your thinking before writing. They're evaluating your problem decomposition, not just whether the query works. I broke it into CTEs step by step and that worked well.
Product/case round: They give you a scenario tied to a Lyft metric. Mine was: 'driver utilization has dropped 8% month-over-month in three markets. how would you investigate this.' Classic diagnostic framework: is it real or a data artifact, is it supply or demand, is it a specific cohort or market-wide, what's the leading indicator.
They push you on your assumptions. When I said 'I'd check if the change is in the data pipeline first,' they asked how I'd do that and what signals I'd look at. Be ready to go one level deeper on every step.
Stats round: A/B testing design plus some probability. Questions I got: how would you design an experiment to test a change to the driver incentive structure (tricky because drivers are not IID, their behavior is correlated), and a Bayesian updating problem. Also one question about how to detect a metric that's moving due to composition shift vs. real change.
If you know A/B testing well for a marketplace, you'll be fine. If you only know the basic two-sample t-test setup you might struggle with the interference and spillover pieces.
Overall the Lyft DS interview is rigorous. More stats depth than most companies at this level. Prep window functions, product diagnostic frameworks, and experiment design for two-sided markets.