Instacart
Senior
Own search relevance analytics and experimentation for Instacart's core query-to-cart funnel
Instacart is hiring a Senior Data Scientist dedicated to Search within Shopping Experience, owning the analytics and experimentation strategy for how customer intent maps to relevant items and retailers. The role partners with Product, Engineering, and ML on ranking, retrieval, and search UX, connecting offline model evaluation to online business metrics like search conversion, order rate, and GTV. Interview should probe search-specific experimentation design, diagnostic analysis by query/segment, and bridging offline ML metrics with online outcomes.
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What this interview tests
- Search funnel ownership: query, impression, engagement, cart-adds
- Experimentation on ranking, retrieval, and search UX changes
- Diagnostic analysis by query class, price point, surface, lifecycle
- Bridging offline model evaluation (NDCG, precision/recall) with online metrics
- Advanced SQL and A/B test design (power, sample size, covariate adjustment)
- Causal inference beyond standard A/B tests
Common question themes
Design an experiment for a new search ranking model with ambiguous results
How would you diagnose a drop in search conversion for a specific query class
How do you validate that an offline NDCG improvement helps real users
Walk through a metric definition or instrumentation fix you drove
How do you balance relevance, monetization (ads), and latency trade-offs