Went through the SpaceX data scientist interview process earlier this year. Five rounds total, spread across about three weeks. Sharing the breakdown because I couldn't find much specific info when I was prepping.
The recruiter screen was pretty standard. 30 minutes, walk through your background, why SpaceX, what kind of problems you've worked on. No technical content yet.
SQL round: This was the meatiest technical screen. Two medium-hard SQL problems. One involved window functions, specifically ranking launches by some metric within groups. The other was a join-heavy query across a few tables to pull a time-series aggregation. They weren't trick questions but you need to be fluent with CTEs and window functions. Stumbling on syntax costs you.
Stats + case study: One round that blended both. First half was classical statistics: A/B testing setup, sample size estimation, what to do when your assignment mechanism isn't perfectly random. Second half was a product case. They give you a scenario (think: you have sensor data from a test, something's off, how do you investigate). It's more about structured thinking than a specific answer.
Data take-home: About 4 hours of work. Real telemetry-style dataset. They want you to explore it, surface findings, build one or two visualizations, and communicate recommendations clearly. Python or R, your choice. I used pandas + matplotlib.
Hiring manager interview: Mostly behavioral but also dug into my past work. Heavy on "tell me about a time you had imperfect data" and "how did you communicate results to non-technical stakeholders." STAR format. Be specific.
For prep: SQL on LeetCode or StrataScratch (medium level), brush up on experiment design, and practice explaining your project work to someone who isn't a data scientist. That last one gets people more than the coding.