Research Scientist - Demand Forecasting

Job description

Meta runs a large-scale server fleet, and every product bet (AI training, ranking, inference, storage) turns into a demand for compute that we must forecast, shape, and match to a physical supply of servers, racks, power, and data center space. As a technical owner within Server Demand Planning, you drive server demand forecasting and demand-supply matching for your area and close on feasible supply requirements: you forecast long- and near-term server capacity demand by rack/hardware type and region, and you build the operations research models that match that demand to supply so we land the right long term DC & hardware infra requirements, in the right place, at the right time.

You choose the right modeling approach for the problems you own, deciding when to ship a production-grade optimization system versus a fast lightweight model to unblock a decision, and you partner closely across product/service capacity owners, capacity engineering, supply chain, data center planning, and finance.

Responsibilities

• Own and drive a multi-horizon server/MW demand forecast (near term through 2-5+ years), producing trusted, reproducible releases to inform decisions Aggregate and normalize demand signals from short term demand and product groups, into a single trusted statistical long term demand plan Formulate and solve the demand-supply matching problem using operations-research models that reconcile forecasted demand with hardware roadmaps, cooling, lead times, and power constraints to inform the Plan of Record
• Build across the full modeling spectrum: production-grade optimization and forecasting systems and prototype models and heuristics that answer a leadership question or unblock a decision
• Partner with data center engineering, site selection, and hardware strategy teams to align next-generation data center designs, rack sizing, and hardware roadmaps with demand, eliminating stranded power, cooling, and space capacity

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 6+ years applying operations research / management science to real planning problems in demand planning, capacity planning, supply-demand matching, network optimization, or inventory MS in a quantitative field (Operations Research, Industrial Engineering, Applied Math, Statistics, CS, or related), or equivalent experience Deep operations-research toolkit: mathematical optimization (LP, MILP, stochastic/robust optimization), simulation, queuing theory, and probabilistic/statistical forecasting
• Demonstrated ability to build BOTH production-grade models/systems (deployed, maintained, driving real decisions) AND lightweight/prototype models delivered fast under ambiguity
• Strong demand-to-supply matching experience: reconciling forecasted demand against constrained supply, lead times, and inventory Experience with optimization solvers (Gurobi, CPLEX, Xpress, or OR-Tools) and with SQL + Python for modeling, analysis, and pipelines Publications, patents, or recognized technical leadership in OR / optimization / forecasting PhD in Operations Research, Industrial Engineering, Management Science, or a related quantitative field
• Direct experience with server/compute or data center capacity planning at hyperscale Statistical/ML forecasting depth (time series, hierarchical/probabilistic forecasting, forecast reconciliation)