Responsibilities

Own the analytical strategy for a product area: identifying the highest-leverage questions, defining the measurement framework, and ensuring data-driven decisions
Serve as a strategic partner to product and engineering leadership, translating ambiguous business problems into analytical approaches and clear, actionable recommendations
Define north-star metrics and measurement strategies to set goals, evaluate progress, and make trade-offs
Design and oversee experiments and causal analyses, ensuring methodological rigor and that results drive real product decisions
Develop and maintain a deep understanding of user growth dynamics: how acquisition, activation, and retention interact to drive growth, and use that understanding to diagnose metric movements, explain trends to leadership, and anticipate emerging risks or opportunities
Contribute and own areas of the team’s forecasting and growth modeling efforts, helping translate statistical models into actionable growth strategies
Mentor and elevate other data scientists through code review, methodology guidance, and establishing reusable analytical frameworks
Represent data science in cross-functional forums, making the case for what the data shows even when it challenges prevailing assumptions
Drive alignment across data science, data engineering, and product on shared priorities like data quality, metric definitions, and instrumentation

Requirements

8+ years of experience in data science, analytics, or applied quantitative analysis, with a track record of shaping product strategy through data
Demonstrated ability to lead complex, cross-functional analytical initiatives from problem framing through stakeholder alignment to decision
Deep expertise in experimentation (A/B testing) and causal inference, with strong judgment about when each method applies and what conclusions they support
Advanced proficiency in SQL and Python for analysis, modeling, and validation
Experience defining and owning product metrics that teams actually use to make decisions
Strong opinions, loosely held: you can take a position on what the data says, advocate for it clearly, and update when the evidence changes
Track record of mentoring or technically leading other data scientists