Data Scientist, Infrastructure Finance

Meta is seeking an experienced Data Scientist to support Infrastructure Finance. The role will build analysis, models, and decision frameworks that connect planning, financial models, and utilization data to capacity planning and operational practice, with cross-functional partnership to improve the cost and ROI of infrastructure investments.

Responsibilities

• Develop and own analytical models and decision frameworks that convert utilization, demand, performance, cost, and capacity constraints into metrics and scenarios for multi-year capacity plans, investment priorities, and efficiency goals
• Independently identify, size, and pressure-test utilization and efficiency opportunities in ambiguous problem spaces
• Partner with Infrastructure Planning, Capacity Engineering, and Operations to embed recommendations into planning assumptions, goals, and operating reviews
• Partner with Infrastructure Data Science, Infrastructure Finance, and Product Finance to align data definitions, analytical methods, and financial implications, and to set standards for model validation, documentation, auditability, and reproducibility
• Synthesize complex analysis into clear recommendations for VP and executive stakeholders, influencing cross-functional decisions without direct authority

Requirements

• Degree in a quantitative field (Engineering, Math, Science) or equivalent practical experience
• 10+ years of experience applying analysis, data science, statistics, economics, or operations research to business and investment decisions
• Experience applying data science to operational planning, resource allocation, or efficiency decisions, including carrying ambiguous work from problem definition through implementation and measurable outcome
• Experience translating scenario and sensitivity models into decision tools used by business partners, including spreadsheets
• Experience using SQL and Python (or equivalent tools) to independently analyze large, messy datasets and build, maintain, and improve reusable analytical models
• Experience communicating quantitative recommendations to executives and influencing decisions across organizations

Preferred Qualifications

• Demonstrated ability to integrate AI tools to optimize or redesign workflows and drive measurable impact, such as efficiency gains or quality improvements
• Experience adhering to and implementing responsible, ethical AI practices, including risk assessment, bias mitigation, and quality and accuracy reviews
• Demonstrated ongoing AI skill development, including prompt or context engineering and agent orchestration, and staying current with emerging AI technologies
• Experience evaluating ROI, marginal cost, cost-to-serve, and capital-allocation trade-offs
• Demonstrated use of AI tools to accelerate analytical workflows and improve work quality, with responsible practices for validation, reproducibility, and sensitive-data handling
• Experience with forecasting, scenario modeling, uncertainty quantification, and causal inference or econometrics
• Experience with infrastructure planning, capacity engineering, operations, cloud or compute economics, or other capital-intensive systems
• Familiarity with AI infrastructure economics and data center constraints, including training and inference cost drivers, accelerator utilization, power, and cost-performance-utilization trade-offs across CPUs, GPUs, and storage
• Familiarity with data center, semiconductor, cloud, server, networking, and software system architecture

Skills and Tools

• SQL
• Python

Role Location and Compensation

Location: Menlo Park, CA (onsite)

Compensation: USD 210,000 - 281,000 per year