Central Product Platform (CPP) exists to make product success and innovation at Meta better and faster. CPP builds the platforms every product group runs on: the Experimentation Platform behind tens of thousands of tests and most launch decisions, the Research Platform that turns user research into an agent-accessible asset, the Metrics Platform that defines Meta's core metrics, and the analytics, design, and communication platforms teams use to answer questions and ship. AI is the lever reshaping all of it — analytics is going agent-native, research is becoming queryable in natural language, and experimentation is being rebuilt so AI can propose, run, and read tests. This role is a DS Tech Lead who operates across CPP, taking the toughest measurement problems head-on.
We are seeking a Data Scientist at the principal level to serve as a thought leader and strategic analytics partner for the company. platforms Meta's product teams rely on to research, experiment, and decide what to build. In this role, you will define the analytical vision across these platforms, shaping how Meta measures success, sizes opportunities, and makes data-driven decisions across complex, cross-functional initiatives. You will partner directly with executive leadership to translate ambiguous business questions into rigorous analytical frameworks, set the bar for what a strong metric, evaluation, and causal claim looks like, and establish the standard for how data science is practiced across the organization.
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
Minimum 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 Advanced degree in Statistics, Economics, Computer Science, Mathematics, or a related quantitative discipline
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Experience with large-scale distributed data querying technologies such as Hive, Presto, or Spark in a production analytics environment
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Track record of operating as a principal-level individual contributor embedded across multiple concurrent product bets in a high-ambiguity environment
- Demonstrated experience integrating AI tools and agent-based workflows into analytical processes, with measurable improvements in output quality or efficiency