Senior
Build the statistical measurement foundations for unsafe-content prevalence at Pinterest scale
Pinterest is hiring a Senior Data Scientist to design sampling frameworks and measurement methodologies that track Trust & Safety policy violations across complex, multi-component user interactions on a platform with 500M+ monthly users. This interview goes deep on statistical sampling design, large-scale data pipeline construction, and the judgment to turn ambiguous safety policy into rigorous, defensible prevalence metrics that executives will act on.
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Free · a live voice mock calibrated to this exact role
What this interview tests
- Statistical sampling design for prevalence measurement
- ML-assisted sampling and up-sampling for rare/complex events
- Large-scale data pipeline design (Python/SQL/Spark) for safety labeling
- Translating ambiguous policy into unified LLM prompts and labeling instructions
- Calibrating labeler/BPO decision quality
- Driving ambiguous, cross-functional measurement projects end-to-end
Common question themes
Design a sampling framework to measure prevalence of a specific policy violation
Handle multi-component interactions as distinct measurement units
Translate a written safety policy into a labeling instruction or LLM prompt
Calibrate BPO labeler quality when decisions are inconsistent
Defend a metric's validity before it reaches executive leadership
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