Lyft data science interview with a meaty two-part case: diagnose a sudden metric change, then design an A/B test around the fix. The kind of question that sounds manageable until you're actually in it and realize how many moving parts there are.
“I started with time-based decomposition, which felt right, but I jumped to conclusions too fast about what segment was responsible before actually checking the data.” The rest of the author's notes on Data Scientist interview at Lyft, Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.