Been doing a parallel track of interviews: two big tech companies and three Series B/C startups. Wanted to share what the actual question types look like because the gap is bigger than I expected.
Big tech (one FAANG, one large fintech):
Both had dedicated SQL rounds. These weren't 'write a select statement' tests. We're talking multi-step analytical questions: build a 7-day rolling retention metric, identify drop-off points in a funnel with messy event data, handle duplicate rows from a pipeline bug. One company gave me a 30-minute take-home SQL problem before the loop even started.
ML system design was real. I had to design an experimentation platform for a two-sided marketplace: how do you detect network effects in A/B tests, what's your variance reduction strategy, how do you set up guardrail metrics. This required actual knowledge, not just ML jargon.
Probability and stats were light in both. One question on expected value, one on Bayesian updating. Less than I expected.
Startups (all Series B or C, 150-600 employees):
No dedicated ML system design rounds. One startup asked me to walk through a model I had built and how I would productionize it, but it was conversational, not structured.
SQL was simpler but they added a 'now tell me what you'd do with this result' step that big tech didn't do explicitly. They want you to connect the output to a business decision in real time.
Product sense was heavy at all three. At one company the founder joined the last interview and asked me to diagnose a drop in a key metric we'd never discussed. Just from the numbers. No context.
One startup asked me about causal inference in the context of a pricing experiment. That surprised me.
Comp rough ranges: Big tech: $160k-$215k base, MLE-adjacent data science roles. The spread is huge. Startups: $130k-$165k base, meaningful equity that mayor may not matter.
Happy to answer specific questions about any of the rounds.