TikTok·Data Scientist·Technical Phone Screen·Senior

Senior

Jun 2025Remote

Summary

TikTok data scientist interview with a deep metric investigation case. One question, but it was a beast. The kind of problem where you think you know where to start and then realize halfway through that you forgot three entire layers of the analysis.

Questions Asked (1)

The Ads Manager dashboard shows a 15% relative drop in ad-creation completion rate on a specific day compared to the prior 7-day baseline. Walk through how you would triage and investigate this: verify the drop is real, localize where it's coming from, determine root cause, quantify impact, and decide whether to roll back or continue.

Product Analytics & MetricsRoot Cause AnalysisA/B Testing & Experimentation

Author's notes

This question is basically five questions duct-taped together and they want all of them.

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AI HintsAI Generated

Suggested Approach

Start by validating the drop's statistical significance and ruling out data pipeline issues, then segment the funnel to localize the drop by platform, user cohort, and step. Investigate potential causes (e.g., recent releases, external events) and quantify impact on downstream metrics, then decide on rollback or further experimentation based on severity and business impact.

Pro tip: Always check for data quality issues first—many apparent metric drops are due to logging errors, tracking changes, or pipeline delays. Also, consider seasonality and day-of-week effects; compare to the same day last week, not just the prior 7-day average.

1. Verify the drop is real

Check data pipeline health, logging, and tracking to rule out instrumentation issues. Confirm the drop is statistically significant and not due to random variation or seasonality.

2. Localize the drop

Segment the ad-creation funnel by dimensions like platform (iOS/Android), app version, region, user type (new vs. existing), and step (e.g., start, form completion, submit). Identify which segment(s) drive the drop.

3. Determine root cause

Correlate with recent changes (releases, experiments, backend updates) and external factors. Use funnel analysis, session recordings, or user feedback to pinpoint the cause.

4. Quantify impact

Estimate the drop's effect on downstream metrics (e.g., total ads created, revenue) and assess whether it's within acceptable bounds or requires immediate action.

5. Decide rollback or continue

If the cause is a recent change and impact is significant, recommend rollback. If impact is minor or cause is external, monitor and possibly continue with mitigation.

Key Points to Mention

Statistical significance testing (e.g., t-test, confidence intervals) to confirm the drop is not noise.
Funnel segmentation by dimensions (platform, app version, region, user cohort) to localize the issue.
Correlation with recent releases, A/B tests, or external events (e.g., holidays, outages).
Quantification of business impact (e.g., revenue loss, user churn) and prioritization.
Decision framework for rollback vs. continue, considering severity, reversibility, and learning value.
Communication with cross-functional teams (engineering, product) to align on next steps.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.