Netflix Data Scientist interview experience

Instead of vague definitions, I was asked to reason through primary, secondary, and guardrail metrics in a causal inference and experimentation context. The interviewer was friendly and seemed genuinely sharp about the subject.

The technical screen felt like it was testing how I thought about designing experiments and interpreting results, especially when you’re balancing goals against safety rails. I didn’t end up receiving an offer. Still, the experience left me with the impression that the team cared about the details of decision-making—how you structure metrics and why those choices