sat the robinhood DS loop a couple months ago, L4/L5 level. sharing my notes.
robinhood's DS interview is pretty thorough compared to some fintech companies. it's not just SQL and business case: they actually test statistics and ML concepts, which i appreciated.
what i saw across the rounds:
SQL (60 min): this was a real SQL interview. not 'write a basic join.' questions involved window functions, running totals, lag/lead for time series, and one question about identifying suspicious transaction patterns using self-joins. tables were clearly inspired by their actual data model (user trades, positions, balances). know window functions cold.
case / product analytics (45 min): given a user funnel for a new feature rollout, where would you look if conversion dropped 15% week over week? walked through my diagnostic framework: check if the data is right first, then segment. they pushed on A/B testing design: what's your minimum detectable effect, how long do you run the test, how do you handle novelty effect.
statistics / experimentation (45 min): this was the round that filters a lot of people. questions included: what is p-value and why is it often misunderstood, explain confidence intervals to a non-technical stakeholder, how do you handle multiple testing corrections, what would you do if your experiment shows different effects across user segments. also asked about power analysis.
ML concepts (30 min light): not a deep ML round but they asked me to walk through a model i built, explain feature selection choices, and how i'd handle class imbalance. this felt more like a conversation than a test.
behavioral (30 min): standard. influence without authority, prioritization, communicating ambiguous findings upward.
the overall bar: strong SQL, strong stats/experimentation, product curiosity. you don't need to be a deep ML engineer but you need the fundamentals.