Went through a Brex DS interview loop targeting a mid-level data scientist role in Q1 2026. Four rounds total. Going to break this down because the mix was different from what I expected.
Round 1: SQL Two questions, roughly 45 minutes. The first was a window functions problem on a transaction dataset, something like "find customers whose spend increased more than 20% month over month for three consecutive months." Tricky to write cleanly but not algorithmically hard. The second was a funnel analysis query. Know your CTEs, window functions, and how to calculate conversion rates in SQL.
Round 2: Stats + Probability This was more rigorous than I expected for a startup. They asked a Bayesian inference question (not trick-question style, but genuinely applied: "your fraud model flags a transaction, the model has X precision and Y recall, how do you think about the posterior probability of actual fraud?"). Also a question about A/B testing assumptions, specifically whether the standard z-test is appropriate when your metric is revenue per user at a company with heavy right-tailed distributions.
Round 3: Case / Product Sense They gave me a hypothetical scenario: Brex card spend dropped 15% last month. Walk through how you'd diagnose it. This is classic but they pushed hard on how I'd distinguish internal product issues from macroeconomic signals and from a data collection problem. The data quality angle is where a lot of DS candidates thin out.
Round 4: Behavioral Standard, focused on cross-functional collaboration and how you've influenced product decisions with data.
Salary for mid-level DS in SF market was in the $170-210k TC range based on what I've seen, though I can't share my specific number.
The SQL and stats rounds were the real filters. If you're weak on either, prep those first.