Job description

Meta runs one of the largest server fleets on earth, 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 near- and long-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. Own and drive a multi-horizon server/MW demand forecast (near term through 2-5+ years), producing trusted, reproducible release 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 IDC 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 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 Experience with demand-to-supply matching: reconciling forecasted demand against constrained supply, lead times, and inventory Fluency with optimization solvers (Gurobi, CPLEX, Xpress, or OR-Tools) and with SQL + Python for modeling, analysis, and pipelines Direct experience with server/compute or data center capacity planning at hyperscale PhD in Operations Research, Industrial Engineering, Management Science, or a related quantitative field Experience with planning platforms (Kinaxis, SAP IBP, o9, Blue Yonder, Demantra, E2open) and internal capacity tools (MCP/ICPC, Capacity Explorer) Statistical/ML forecasting depth (time series, hierarchical/probabilistic forecasting, forecast reconciliation) Publications, patents, or recognized technical leadership in OR / optimization / forecasting

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Platform: meta
Effort: medium