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.

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 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