Design station experiment with interference and rush-hour spillovers
Company: Uber
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: hard
Interview Round: Onsite
Overview: This question evaluates a data scientist's competency in experimental design and causal inference under interference and non-stationarity, covering skills such as randomization unit and period selection, KPI and guardrail specification, synthetic control construction, variance-reduction techniques, and diagnostics for spillovers and temporal effects. It is commonly asked in the Analytics & Experimentation domain to assess the ability to design robust, real-world experiments and is primarily a practical application that also requires strong conceptual understanding of bias, carryover, seasonality, shocks, and validation methods.
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