went through the Starbucks DS interview loop for a data scientist II role on the personalization team (Seattle-based, some remote flexibility). took notes throughout because the process was more involved than i expected for a retail/CPG company.
full loop timeline: recruiter screen -> technical screen -> final panel (4 hours)
technical phone screen (60 min): two SQL problems and a stats/probability question. the SQL was on a HackerRank-style interface, Postgres flavor. one was a straightforward aggregation with a window function (rank within a partition). the second was a multi-table join to answer something like: which reward tiers have the highest variance in visit frequency over 90 days. not hard, but you need to be fast and clean.
the stats question was a classic: explain p-value to a non-technical stakeholder, then: if we run an A/B test on the loyalty program and see p=0.04, should we ship? they wanted to see that i'd ask about: power, sample size, primary vs secondary metrics, and whether the test was designed properly before i just said "yes p<0.05 ship it." that trap is a real filter.
final panel:
case round (90 min): a data-heavy case. scenario: engagement with the Starbucks Rewards program dropped 8% YoY in a specific demographic. walk me through how you'd diagnose it. this is a classic DS case and they went deep. expect to be asked: how you'd segment the data, what SQL you'd write to validate a hypothesis, and what the business action would be even before you fully confirm the root cause.
ML/modeling round (60 min): how would you build a propensity model to predict which users are at risk of churning from Rewards? feature engineering, model choice, how you'd evaluate offline vs online, how you'd set a threshold for intervention. pretty standard ML interview content but they pushed on how you'd tie model outputs to actual marketing/ops actions.
behavioral round (30 min): mostly about cross-functional collaboration with marketing and finance. they run data science very collaboratively with stakeholders here, not as a pure research function.
comp (from my offer): base $145k, 10% target bonus, some RSUs. Seattle, data scientist II level, 3 YOE in DS. not Google-tier but honestly solid for the role scope.