Start by framing the decision as a trade-off between reducing bad seller exposure and potential collateral damage to good sellers and buyer experience. Then walk through a structured experiment design: define treatment precisely, choose randomization unit, select guardrail and success metrics, plan ramp with uncertainty quantification, and pre-specify heterogeneity checks. Emphasize that you would not launch without clear evidence of net positive impact and acceptable risk.
Pro tip: Propose a phased approach: first run a small-scale A/B test with a conservative downranking threshold, then use Bayesian methods to quantify uncertainty and decide whether to expand. This shows you balance speed with rigor and avoid overreacting to noisy early results.
Specify exactly how suspected bad sellers are identified (e.g., model score threshold) and what downranking means (e.g., multiply ranking score by 0.5). Choose randomization unit (e.g., seller, listing, or user) based on interference risk and analysis goals.
Choose primary success metrics (e.g., reduction in bad seller impressions, increase in good seller GMV) and guardrail metrics (e.g., overall search CTR, buyer satisfaction, false positive rate). Ensure metrics are sensitive and aligned with long-term goals.
Plan sample size, duration, and ramp stages (e.g., 1% -> 5% -> 20%). Use sequential testing or Bayesian methods to monitor early signals while controlling error rates. Pre-register analysis plan.
Account for uncertainty in the bad seller model (e.g., via bootstrap or posterior sampling) and test for heterogeneous treatment effects across seller categories, user segments, and query types. Pre-specify subgroups to avoid p-hacking.
Evaluate results against pre-defined success criteria, considering both statistical and practical significance. If positive, launch with monitoring; if not, iterate on model or treatment. Document learnings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.