Went through Bloomberg's DS interview loop for a mid-level data scientist role in Q1 2026. There is almost nothing DS-specific about Bloomberg's process online, so here's the full picture.
Rounds I had: Recruiter screen (15 min, logistics) Technical phone screen: 45 min. Two SQL problems + one stats conceptual question. That's it. Onsite (virtual): 4 rounds. Advanced SQL / analytics Stats + experiment design Case-style product analytics Behavioral
SQL specifics: Bloomberg's SQL questions are not toy queries. Both in the phone screen and onsite, they wanted window functions, CTEs, time-series aggregations. One question involved calculating a running P&L across multiple instruments. If you haven't used window functions in real work, you need to practice them explicitly. RANK(), LAG(), cumulative SUM() over partitions all came up.
Stats questions: "Walk me through how you'd design an A/B test for a change to how we display a data metric." "What's the difference between statistical significance and practical significance? When have you had to explain this to a stakeholder?" "What's a situation where a model was accurate but not useful?"
Case-style analytics: They gave me a made-up scenario: Bloomberg launched a new analytics feature, DAU is down 10% over 3 weeks. Walk me through how you'd diagnose. Standard product analytics case, but they pushed hard on instrumentation: "What data would you need that you might not have?" That question separates people who've only done clean-data analysis from people who've actually shipped analytics.
What they care about: rigor in stat reasoning, SQL fluency, and whether you think in business terms not just model terms. They're not looking for deep ML at this level. It's more applied analytics, with some experimentation.
Comp (offer I got, June 2026, NYC): total comp around $185K including base + bonus. Base was $145K. No equity at this level in DS, which surprised me.