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Evaluate a new product with experimentation

StripeData Scientist · Technical Screen

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This question evaluates experimental design and causal inference skills, including defining an Overall Evaluation Criterion (OEC) and guardrail metrics, selecting an appropriate test design and ramp/power strategy, specifying quasi-experimental fallbacks, and performing metric diagnostics for a recommendation module in a commerce app.

A new recommendation module may cause cross‑user interference and traffic seasonality. Design an evaluation plan. (1) Define an Overall Evaluation Criterion (OEC) for a commerce app and 3 guardrails (e.g., churn, latency p95, complaint rate) with precise formulas and units. (2) Choose a test design (user‑level RCT, geo‑cluster, or time‑based switchback) and justify against interference, non‑stationarity, and operational constraints. (3) Describe ramp strategy and pre‑registration: stopping rules, power target, variance reduction (CUPED/covariate adjustment), and small‑area risk controls. (4) If randomization is infeasible, propose a quasi‑experimental fallback (synthetic control or difference‑in‑differences) and list the assumptions and falsification tests you will run. (5) Suppose mid‑test the OEC flatlines while add‑to‑cart rises and conversion falls; provide a metric‑debugging checklist and the exact cuts you will request to localize the issue (e.g., by device, geography, new vs returning, latency buckets). Be specific and write the equations where relevant.