Data Scientist, Finance (Infrastructure & AI)

Meta•Menlo Park, CA

13d•$210,000 - $281,000

About The Position

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.

Requirements

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

Nice To Haves

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

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