Measure notification impact and set guardrails
Company: Meta
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: hard
Interview Round: Technical Screen
You introduce a new notification type. Define a measurement strategy that goes beyond vanity metrics. 1) State precise primary metrics (e.g., 7-day retention uplift, session starts per user-day attributable to the notification, downstream content interactions) and guardrails (e.g., unsubscribes, notification disablement, spam reports, app uninstalls, time-to-first-meaningful-action). 2) Propose an experiment design that accounts for interference (users seeing different volumes at different times), cadence throttling, time-of-day effects, and novelty (use staggered rollouts, notification-level randomization, and per-user holdouts). 3) If revenue isn’t observable at short horizons, define leading indicators and a difference-in-differences plan using notified vs. matched unnotified users. 4) Detail power calculations inputs (baseline rates, MDE, variance inflation from clustering) and the minimum runtime logic. 5) List two analyses you must run before declaring success to ensure no long-term fatigue is being masked by short-term lifts.
Quick Answer: This question evaluates causal inference, experiment design, metric specification and attribution, statistical power calculation, and long-term monitoring skills within the Analytics & Experimentation domain for a data scientist role, testing both practical application (designing experiments, logging, power inputs) and conceptual understanding (interference, novelty effects, and guardrails). It is commonly asked to assess the ability to define precise primary metrics and guardrails, design experiments and attribution strategies that isolate treatment effects while protecting user experience, and plan analyses for short- and long-term impact in analytics and experimentation.