Went through the Slack DS loop earlier this year for a senior data scientist role on the product analytics side. Four rounds. Here's what each covered.
SQL round (45 min, live). This was harder than I expected. Not just basic aggregations. They had multi-table joins, window functions (LAG/LEAD, running totals), and a question about sessionization from raw event logs. If you haven't practiced sessionization logic with LEAD() or gap-and-island tricks, do that before this interview. The dataset was Slack-themed: user events, channel activity, message timestamps.
Product case round (45 min). Given a metric that dropped (DAU for a feature). Walk through your diagnostic framework. They want: disambiguate the metric first (is it the definition or the number), segment (platform, user cohort, region, time), formulate hypotheses, propose measurements. Strong product sense matters here. I was asked "how would you define engagement for Slack as a product" as a follow-up. Think about active users vs. sending users vs. reading users.
Stats + experimentation round (45 min). A/B test interpretation. They gave me results from a hypothetical experiment and asked whether the treatment worked and what I'd do next. Covered: statistical significance, p-values, multiple testing, novelty effects, network effects (Slack-specific: what happens when you treat half a channel?). Network effect contamination is very Slack-specific, be ready for it.
Behavioral + manager fit (45 min). Typical behavioral: influence without authority, communicating uncertainty to non-technical stakeholders, project that didn't land as expected.
SQL was the hardest in terms of pure difficulty. The stats round was conceptually hard but less about memorizing formulas and more about thinking clearly.
For prep: Mode SQL Practice, Stratascratch for window functions, and really understand experiment design for network products specifically.