Director, Data Science - Central Product Platform (CPP)

Responsibilities

• Define and drive the analytical strategy for high-priority product areas, establishing measurement frameworks and success metrics where no established playbooks exist
• Partner directly with VP-level and cross-functional leaders to synthesize complex quantitative analyses into clear, actionable narratives that shape product and business strategy
• Lead the design and execution of large-scale experimentation programs, including causal inference methodologies and A/B testing frameworks, to evaluate product impact and inform investment decisions
• Develop and maintain predictive models and forecasting systems that inform product roadmap prioritization, opportunity sizing, and long-term growth strategy
• Establish company-wide best practices for analytical design, data collection methodology, and statistical rigor, and drive adoption of these standards across data science teams
• Identify and frame ambiguous, long-horizon business problems by collaborating with cross-functional leaders across product, engineering, and operations to align on research questions and hypotheses
• Design and champion self-service data exploration interfaces and visualization standards that enable scalable, democratized access to product insights across the organization
• Serve as an internal and external thought leader in quantitative methods, contributing to the evolution of forecasting, prediction, and causal analysis capabilities at Meta
• Mentor and elevate other data scientists and cross-functional partners through exemplar work, coaching on analytical craft, and propagating learnings across teams
• Redesign analytical workflows to fully leverage AI tools and agents, modeling how data science practitioners can integrate AI as a force multiplier for quality and speed

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 12+ years of experience in data science, quantitative analytics, or a related field, with demonstrated impact on product strategy at company scale
• Experience defining measurement frameworks, success metrics, and analytical strategies for complex, ambiguous product domains with significant business impact
• Experience applying advanced statistical methods including causal inference, experimentation design, predictive modeling, and time series forecasting in a product analytics context
• Experience influencing executive and cross-functional stakeholders through written analytical narratives, data presentations, and strategic recommendations
• Experience establishing analytical standards, best practices, or data infrastructure patterns that have been adopted broadly across an organization