Principal Data Scientist - Rentals Shopping

• Drive the long-term data strategy for the Rentals Shopping pillar, using deep analysis of consumer behavior and market dynamics to identify high-impact growth opportunities and inform the product roadmap
• Elevate statistical rigor, modeling, and experimentation practices across Rentals to enable high-confidence decision-making
• Partner with product, engineering, and design leaders to translate complex analysis into clear, actionable product and business strategies
• Lead analytical and modeling work across critical problem spaces such as rental search relevance, ranking, personalization, or marketplace efficiency
• Design and guide robust A/B tests and other experimentation frameworks to measure the impact of new features and optimizations in the rental shopping experience
• Apply modern AI (including LLMs and agentic workflows) to increase data science velocity, improve model quality, and scale intelligent user-facing solutions across the Rentals marketplace
• Coach and mentor data scientists, providing technical leadership and helping to unblock complex analytical and modeling challenges

• Ph.D. or Master's degree in a quantitative field (e.g., Statistics, Economics, Computer Science), or equivalent practical experience
• Substantial experience (typically 8+ years) in quantitative analysis, advanced modeling, and experimentation at scale in consumer-facing technology environments
• Expert in SQL and Python, with experience applying statistical modeling (e.g., causal inference, time series) in big data environments such as Databricks or Snowflake
• Proven track record of shaping long-term product roadmaps and driving meaningful business outcomes through data-driven insights
• Ability to influence senior cross-functional leaders and turn complex analysis into clear, executive-ready recommendations and action plans
• Comfortable leading in a complex business area with significant autonomy, setting direction and driving execution across partners

Transferable skills or related experiences

Equity awards based on factors such as experience, performance and location