Overview

Instacart’s Shopping Experience team is focused on making it fast and effortless for customers to find the right items within a single retailer and complete their order with confidence. As a Senior Data Scientist dedicated to Search, you’ll own the analytics and experimentation strategy that powers how we interpret customer intent and connect it to the most relevant items and retailers.

In this role, you’ll partner closely with Product, Engineering, and Machine Learning to shape the roadmap for search relevance, ranking quality, and latency. Your work will translate complex, noisy signals into clear insights and recommendations that move the metrics that matter—search conversion, order rate, and GTV—while also strengthening downstream experiences like ads and retailer satisfaction.

About the Job

• 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.

About You

You combine rigorous analytics, strong product sense, and clear communication to drive decisive action in a fast-paced environment. You enjoy rolling up your sleeves, collaborating across disciplines, and using experimentation to uncover what truly helps customers find the right items quickly.

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.

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