Senior Data Scientist - Shopping Experience (Search)

In this role, you will own and evolve the analytics and experimentation framework that powers the search experience, ensuring strong alignment between customer behavior, product changes, and business outcomes. You will be responsible for measuring and improving search performance while driving insights that guide roadmap decisions.

• Own end-to-end search and funnel metrics (query, impression, engagement, cart, conversion), including defining guardrails and diagnosing performance shifts across segments.
• Design, run, and interpret A/B tests across search ranking, retrieval, and UX changes, translating results into clear, actionable recommendations.
• Partner with Product, Engineering, and ML teams to prioritize initiatives, size impact, and influence the roadmap for search relevance and latency improvements.
• Conduct deep diagnostic analyses across query types, customer cohorts, and product surfaces to identify performance gaps and opportunities.
• Align offline model evaluation with online experimentation, ensuring improvements in ML models translate into real customer and business impact.
• Improve metric definitions, data quality, and instrumentation to strengthen consistency and trust in search performance reporting.

Requirements

The ideal candidate brings strong technical depth in data science, experimentation, and large-scale product analytics, combined with the ability to translate complex findings into clear product direction. You are comfortable working in fast-paced, cross-functional environments where your insights directly shape user experience.

• 5+ years of experience in data science or product analytics for consumer-facing or marketplace products.
• Advanced SQL skills and experience working with large-scale datasets in modern data warehouses (e.g., Snowflake, BigQuery, Redshift).
• Strong programming skills in Python or R for analysis, experimentation, and statistical modeling.
• Proven expertise in designing and analyzing A/B tests, including statistical rigor, metric design, and decision-making under uncertainty.
• Ability to define success metrics, break down ambiguous product problems, and deliver clear recommendations to technical and non-technical stakeholders.
• Strong communication skills with experience presenting insights that influence product and engineering decisions.
• Bachelor’s degree in a quantitative field (or equivalent practical experience).
• Familiarity with AI-assisted analytics tools to accelerate workflows while maintaining high analytical standards.

Preferred experience includes search relevance, ranking systems, recommendation engines, NLP or semantic search, and advanced causal inference methods such as quasi-experiments or diff-in-diff.

Benefits

• Competitive base salary aligned with US regional benchmarks ($161,000–$204,500 depending on location)
• Equity grants and annual refresh opportunities
• Remote-first work flexibility (Flex First model)
• Comprehensive health, dental, and vision insurance
• Paid time off and company holidays
• Retirement savings plans and additional financial benefits
• Support for professional development and learning
• Access to modern tooling and strong experimentation infrastructure
• Inclusive and collaborative work environment focused on impact and innovation