Data Scientist, Product Analytics

In this role, you will operate as a company-level expert in product analytics, partnering with Product, Engineering, and cross-functional leadership to translate complex, large-scale behavioral data into actionable insights that shape the experiences of billions of people worldwide. You will lead the design of rigorous analytical frameworks, advance forecasting and predictive modeling capabilities, and influence the highest-priority product investments through data-driven storytelling and strategic recommendations.
Data Scientist, Product Analytics Responsibilities
Lead the design and execution of complex analytical projects across product areas, applying advanced statistical methods, causal inference, and experimentation to evaluate product impact at scale
Develop and maintain sophisticated predictive models and forecasting frameworks to inform product roadmaps, goal setting, and resource prioritization
Define and evolve success metrics and measurement strategies for product initiatives, ensuring goals accurately reflect user and business value
Identify and size high-impact product opportunities by synthesizing behavioral data, qualitative signals, and business context across multiple data sources
Partner with Product and Engineering teams to design and analyze large-scale A/B tests and feature rollouts, translating results into clear recommendations for product direction
Build scalable, self-service data pipelines and visualization interfaces that enable cross-functional teams to explore and act on product performance data independently
Communicate complex analytical findings to diverse audiences including product leadership and executives through concise, compelling data narratives and presentations
Contribute to functional data science strategy by proactively identifying methodological gaps, championing analytical best practices, and setting goals across teams
Mentor other data scientists on analytical design, hypothesis formulation, and quantitative modeling, elevating the overall quality and rigor of the team's work
Collaborate with Data Engineering and Research partners to improve data infrastructure, eliminate bias in data collection, and ensure analytical outputs accurately represent the populations of interest