Data Scientist, Infrastructure Finance

Meta’s Infrastructure Finance team is hiring an experienced Data Scientist to connect utilization and planning inputs with financial modeling and operational decision-making. This onsite role in Menlo Park, CA focuses on building analysis, models, and decision frameworks that help improve the cost and ROI of Meta’s infrastructure investments.

What you’ll do

• Develop and take ownership of analytical models and decision frameworks that translate utilization, demand, performance, cost, and capacity constraints into metrics and scenarios for multi-year capacity plans, investment priorities, and efficiency goals
• Independently spot, size, and pressure-test utilization and efficiency opportunities in ambiguous problem areas
• Work with Infrastructure Planning, Capacity Engineering, and Operations to incorporate recommendations into planning assumptions, goals, and operating reviews
• Partner with Infrastructure Data Science, Infrastructure Finance, and Product Finance to align data definitions, analytical approaches, and financial implications, while setting standards for model validation, documentation, auditability, and reproducibility
• Summarize complex analysis into clear recommendations for VP and executive stakeholders, influencing cross-functional decisions without direct authority

Minimum qualifications

• Education: Degree in a quantitative field (Engineering, Math, Science) or equivalent practical experience
• Experience: 10+ years 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 taking ambiguous work from problem definition through implementation and measurable outcomes
• Experience translating scenario and sensitivity models into decision tools used by business partners, including spreadsheets
• Experience using SQL and Python (or equivalent) 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

• Ability to integrate AI tools to optimize or redesign workflows and deliver measurable impact such as efficiency gains and quality improvements
• Experience implementing responsible, ethical AI practices, including risk assessment, bias mitigation, and quality and accuracy reviews
• Ongoing AI skill development, such as prompt/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
• Use of AI tools to accelerate analytical workflows while maintaining responsible 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 concepts

Role details

• Location: Menlo Park, CA (onsite)
• Salary: USD 210,000 - 281,000 per year
• Technologies: SQL, Python