Meta is seeking a data science leader to shape data-driven financial strategy across infrastructure and AI. You'll translate advanced modeling into decisions that guide company-wide investment, resource allocation, pricing, and long-term planning, helping leaders act under significant uncertainty. You'll work closely with cross-functional partners across finance, infrastructure, and product teams, including Meta Superintelligence Labs (MSL).
The ideal candidate combines strong analytical skills with business acumen — someone who can navigate ambiguous, early-stage problem spaces, identify where Meta can deploy resources more efficiently or improve pricing and monetization, and translate those insights into quantified opportunities and clear recommendations.
Responsibilities
- Develop and own analytical models and frameworks that inform multi-year infrastructure planning, investment prioritization, and the financial strategy of Meta's AI businesses
- Build frameworks to evaluate ROI on compute, infrastructure, data, and related spend across products, features, and business segments, and use those insights to inform investment and resource-allocation decisions
- Develop a rigorous understanding of the unit economics of Meta's AI products and business models — contribution margin, cost-to-serve, marginal cost, lifetime value, and the trade-offs that drive them — to inform strategy, pricing, and monetization
- Independently identify efficiency, financial, and monetization opportunities — surfacing where Meta can get more from its investments — and rapidly build the analysis to size and pressure-test them, operating with minimal guidance in ambiguous problem spaces
- Partner with finance, infrastructure, and product teams (including MSL) to define success metrics, size financial opportunities, align on technical methodology, and evaluate trade-offs across competing strategic priorities
- Synthesize data into clear, business-relevant recommendations and communicate their implications to VPs and executive stakeholders
- Design rigorous research and hypothesis-testing approaches, and oversee the quality of analytical outputs across finance, infrastructure, and AI-business domains
- Identify and drive adoption of AI-integrated analytics workflows, including orchestrating AI tools to accelerate modeling and analysis
- Lead ad hoc analyses of emerging topics critical to Meta's business and financial strategy
Minimum Qualifications
- Bachelor's degree in a directly related field, or equivalent practical experience
- 12+ years of experience applying statistical and quantitative analysis techniques to drive key business and financial decisions
- Experience shaping and influencing strategy, investment, or monetization decisions — for example in infrastructure, product economics, pricing, or the economics of AI / technology businesses
- Strong applied statistics and quantitative modeling: experimentation, causal inference / econometrics, uncertainty quantification, forecasting, and scenario modeling
- Demonstrated business and economics intuition — a strong grasp of contribution margin, cost-to-serve, ROI, and trade-offs — and a track record of independently identifying financial or efficiency opportunities and driving them to measurable outcomes in ambiguous, fast-moving environments
- Experience communicating data-driven recommendations to executive stakeholders through written and verbal presentations, with a track record of influencing cross-functional decisions without direct authority
- Experience coding in SQL and Python (or equivalent) to independently work through large, messy datasets and to build, maintain, and optimize analytical models at production scale Familiarity with AI/compute cost economics — understanding inference and training cost drivers well enough to translate technical changes into $/token and margin
- Familiarity with data governance best practices for auditability and reproducibility across the analytics stack
- Master's or PhD in a quantitative field (e.g., economics, statistics, operations research, or a related discipline)
- Experience with pricing and demand modeling (elasticity, willingness to pay, packaging, subscription/API pricing) and translating analysis into pricing and monetization recommendations
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- 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 in data science and/or driving strategy at a hyperscaler, frontier AI lab, or large technology company