Own core Search metrics and funnels end to end (e.g., query → impression → engagement → cart adds), including defining guardrails, monitoring performance across platforms and segments, and diagnosing conversion gaps.
Design, run, and interpret experiments across ranking, retrieval, and search UX (e.g., relevance model changes, query understanding, result layouts), turning ambiguous or conflicting outcomes into crisp, data-driven recommendations.
Partner with Product, Engineering, and ML to prioritize opportunities, size impact, and influence the roadmap for relevance, quality, and latency improvements that unlock measurable business outcomes.
Build deep diagnostic analyses by query class, price point, surface, and customer lifecycle to pinpoint where and why Search underperforms and specify concrete changes that will move key outcomes.
Connect offline model evaluation with online and business metrics by collaborating with ML partners on evaluation design, ensuring model changes reliably improve end-user experience—not just offline scores.
Improve data quality, instrumentation, and metric definitions for Search so that teams can reason about performance with clarity, consistency, and speed.

Requirements

5+ years of experience in data science or product analytics, with a track record of impact on consumer-facing products.
Advanced SQL proficiency, including complex joins and window functions, working with large-scale datasets in modern data warehouses (e.g., Snowflake, BigQuery, Redshift).
Proficiency in Python or R for analysis, experimentation, and modeling.
Hands-on experience designing and analyzing A/B tests end to end, including metric selection, power and sample sizing, covariate adjustment, and decision-making under uncertainty.
Demonstrated ability to define success metrics, decompose ambiguous product problems, and deliver clear, opinionated recommendations to Product and Engineering partners.
Excellent written and verbal communication skills; able to tailor complex analyses for both technical and non-technical audiences.
Bachelor’s degree in a quantitative field (e.g., Statistics, Computer Science, Mathematics, Economics, Engineering) or equivalent practical experience.
Comfort using modern AI tooling (e.g., Claude, code assistants, PromptQL) to accelerate analysis, experimentation, and communication while exercising strong judgment on quality and reliability.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data scienceproduct analyticsSQLPythonRA/B testingmetric selectiondata analysismodel evaluationdata quality

Soft Skills

communicationproblem-solvingcollaborationanalytical thinkingdecision-makingrecommendation deliverytailoring analysesjudgment