Data Scientist, Infrastructure Finance

Meta is seeking an experienced data scientist to improve how we plan, utilize and drive ROI from large-scale infrastructure. You will build analysis, models and decision frameworks that connect planning, financial models, and utilization data to capacity planning and operational practice, helping leaders improve the cost and ROI of Meta's compute, storage, data center, and power investments.
This role sits at the intersection of data science, finance, and infrastructure planning. You will partner with Infrastructure Planning, Capacity Engineering, Infrastructure Data Science, Infrastructure Finance, and Product Finance to turn technical and operational signals into clear investment and operating decisions.

Responsibilities

Develop and own analytical models and decision frameworks that translate utilization, demand, performance, cost, and capacity constraints into metrics and scenarios that inform 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 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

Qualifications

Bachelor's degree in a directly related field, or equivalent practical experience
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 and 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 Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
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
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 concepts in data center, semiconductor, cloud, server, networking, and software system architecture