apple

Apple Data Analyst Case Interview (Engineering): Metrics & Experimentation for Apple Services

This Apple-style case simulates partnering with an engineering and PM team on an Apple Services feature (e.g., Apple Music onboarding, Apple TV+ trial conversion, or App Store discovery). Based on real interview patterns at Apple, the case tests: (1) product thinking with a customer-first, privacy-by-design lens; (2) rigorous metric design and guardrails; (3) experiment/rollout strategy under strict privacy/latency constraints; (4) analytical execution with SQL/Python-level reasoning; and (5) crisp storytelling that influences cross-functional partners. What to expect: - Brief from interviewer: "Engagement dropped for a new Apple Music onboarding card in iOS 18. Propose how you’d investigate and improve it while respecting Apple’s privacy principles." You’re expected to lead with clarifying questions, frame the problem, and state assumptions explicitly. - Metric design: Define a North Star (e.g., 28-day engaged subscribers) and diagnostic metrics (activation, trial start, 1/7/28-day retention, skip rates, bounce, latency, crash, battery impact), plus guardrails (customer satisfaction proxies, performance, accessibility, regional compliance). Discuss on-device vs. server-side telemetry, delayed and aggregated reporting, and how to handle opt-in/attenuated identifiers. - Experimentation & rollout: Choose between A/B, interleaving, switchback, or phased ramp. Specify hypothesis, success metrics, MDE/power check, heterogeneity (market, device class, iOS version), novelty and seasonality effects, holdback for long-term impact, and how you’ll mitigate peeking and metric drift. Address metric movement under privacy constraints (e.g., differential privacy noise, small-sample suppression) and propose validation/triangulation. - Analytical execution: Given a lightweight event schema (sessions, exposures, clicks, trials, subscriptions, experiment_assignments), outline SQL/pseudocode to build a funnel, compute retention by cohort, and compare treatment vs. control with guardrails. Call out data quality checks (late events, timezone normalization, app version bucketing, de-duplication). Interpret a small, slightly messy dashboard/table the interviewer may provide and reason about causality vs. correlation. - Root cause and recommendation: Generate plausible drivers (e.g., card placement, copy length, network latency, eligibility rules, localization) and design a follow-up plan that balances customer experience, engineering cost, and confidentiality. Conclude with an executive-ready narrative: decision, expected impact, risks, and next steps. Apple-specific signals the interviewer looks for: deep customer empathy and simplicity, precision and obsession with details, confidentiality-minded reasoning, cross-functional collaboration, and clear, structured communication. Time guidance (typical): 5–10 min clarifications, 15–20 min metrics/exp design, 15–20 min analysis/interpretation, 10–15 min recommendations and trade-offs.

engineering

60 minutes

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About This Interview

Interview Type

PRODUCT SENSE

Difficulty Level

4/5

Interview Tips

• Research the company thoroughly

• Practice common questions

• Prepare your STAR method responses

• Dress appropriately for the role