Uber·Data Scientist·Technical Phone Screen
Jul 2026
Uber data science interview focused entirely on A/B testing around a promotional campaign. Five questions, all connected, building on each other in a way that felt more like a case study than a standard interview. Pretty intense if you haven't thought carefully about experiment design end to end.
- Why would a company want to run a promotional campaign like this, and if it's limited to one city or market, what's the reasoning behind that restriction?
- What metrics would you use to evaluate whether the promotion worked? Walk through primary, secondary, and guardrail metrics.
- How do you figure out the right sample size for an experiment like this? What factors and inputs go into that calculation?
- The PM sets the minimum detectable effect on revenue at 0.5%. What does that actually mean, and how does it change your sample size requirements?
- The experiment shows a +0.3% lift in revenue but it's not statistically significant. How do you interpret that and explain it to the PM?
“I jumped straight to revenue uplift and forgot to talk about the geo-restriction angle until they nudged me.” The rest of the author's notes on Data Scientist interview at Uber, Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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