Senior Data Scientist - Shopping Experience (Search)

Responsibilities

• 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.

Minimum Qualifications

• 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.

Preferred Qualifications

• Experience in search relevance, ranking, recommendations, personalization, or information retrieval (e.g., e-commerce or marketplace search).
• Familiarity with NLP, embeddings, and semantic search, including how to evaluate and iterate on these techniques in production.
• Experience bridging offline evaluation metrics (e.g., NDCG, precision/recall, human evaluation) with online experiments and business outcomes.
• Background in causal inference beyond standard A/B tests (e.g., holdouts, diff-in-diff, quasi-experiments) to measure long-term or cross-surface effects.
• Comfort working across web and native app surfaces, navigating tradeoffs between relevance, monetization, and latency.
• Proven impact improving logging, instrumentation, and metric definitions in complex data environments.

Compensation: Base pay $161,000–$204,500 USD depending on location (e.g., CA/NY: $194,000–$204,500; other states: $161,000–$170,000). Eligible for equity grants and benefits.