Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 7+ 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 Fluency with optimization solvers (Gurobi, CPLEX, Xpress, or OR-Tools) and with SQL + Python for modeling, analysis, and pipelines 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) Experience with planning platforms (Kinaxis, SAP IBP, o9, Blue Yonder, Demantra, E2open) and internal capacity tools (MCP/ICPC, Capacity Explorer) Publications, patents, or recognized technical leadership in OR / optimization / forecastingMeta builds technologies that help people connect, find communities, and grow businesses. As the senior technical owner of Server Demand Planning, you own the demand side of that equation and close the loop with supply: 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 servers, in the right place, at the right time.