tiktok

TikTok Data Analyst Case Interview: Feed Ranking, Feature Impact, and Experimentation

This case simulates a real TikTok Product/Data Analyst interview focused on the For You feed and creator ecosystem. You’ll be asked to evaluate the impact of (1) a recommendation model update and (2) a lightweight sharing feature (e.g., Repost to followers) across multiple markets. The interviewer looks for TikTok-specific product sense, speed, and rigor under ambiguity, reflecting a culture that prizes measurable impact, safety, and global scale. What you’ll cover at TikTok: - Metric design for short‑video: define primary metrics (e.g., average watch time per user/session, video completion rate tiers like 2s/6s/VCR, session starts, D1/D7 retention), creator health (upload frequency, share rate, unique creators watched), and trust & safety guardrails (report/block rate, policy‑violation exposure, latency p95/p99). - Experimentation at scale: propose an A/B plan, power assumptions, identify/diagnose SRM, handle multiple comparisons, segment by market/age/device/new vs. tenured users, and reason about novelty/learning effects and ramp strategy. Discuss guardrail trade‑offs when watch time rises but report rate or latency worsens. - SQL‑driven analysis: given event schemas (views, likes, shares, follows, reports), write queries/pseudocode to (a) compute per‑user VCR and watch time distributions, (b) build a creator→viewer funnel, (c) detect integrity outliers (sudden spikes in reports or bot‑like activity), and (d) join experiment assignments to outcomes for lift/CI calculation. - Diagnosis & storytelling: investigate a post‑launch anomaly (e.g., APAC watch time ↑ but teen report rate also ↑). Form a root‑cause plan (cohort cuts, feature flags, content type, time‑of‑day), recommend a go/no‑go with clearly stated risks, and outline follow‑ups (holdout, ramp, or rollback criteria). - Global & safety context: reason about localization (different creator/consumer patterns by country), regulatory/safety constraints for young users, and how these shape success metrics and guardrails. Interview style cues typical at TikTok: fast‑paced probing, expectation to quantify assumptions, comfort with messy/partial data, crisp structure (state goal → metrics → design → analysis → risks → decision), and clear trade‑off reasoning between growth and safety. Deliverables commonly include a metric framework, an experiment plan with guardrails, and at least one non‑trivial SQL solution using window functions and sessionization.

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