Research Scientist, Server Demand Forecasting

Meta•Menlo Park, CA

4d•$219,000 - $301,000

About The Position

Meta is seeking a principal technical leader for Server Demand Planning across Infrastructure. The role owns the demand side of the plan and the feasible supply requirements it drives, forecasting long- and near-term server capacity demand by rack and hardware type and region, and building operations-research models to match demand with supply. The goal is to land the right servers in the right place at the right time. The role sets Meta’s technical approach to server demand planning and determines when to build production-grade optimization systems versus fast, lightweight models to unblock decisions. It sets direction across product and service capacity owners, capacity engineering, supply chain, data center planning, and finance; partners directly with organizational leaders; and grows the technical bench. Compensation is $219,000 to $301,000 per year, plus bonus, equity, and benefits.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
At least 8 years applying operations research or management science to real planning problems in demand planning, capacity planning, supply-demand matching, network optimization, or inventory.
Master’s degree in a quantitative field such as Operations Research, Industrial Engineering, Applied Math, Statistics, Computer Science, or a related field, or equivalent experience.
Operations-research toolkit including mathematical optimization (LP, MILP, stochastic or robust optimization), simulation, queuing theory, and probabilistic or statistical forecasting.
Demonstrated ability to build both production-grade models or systems that are deployed, maintained, and used to drive real decisions, and lightweight or prototype models delivered quickly under ambiguity.
Experience reconciling forecasted demand against constrained supply, lead times, and inventory in a planning or capacity environment.
Fluency with optimization solvers such as Gurobi, CPLEX, Xpress, or OR-Tools, and with SQL and Python for modeling, analysis, and pipelines.

Nice To Haves

Direct experience with server or compute capacity planning, or data center capacity planning, at hyperscale.
Experience with planning platforms such as Kinaxis, SAP IBP, o9, Blue Yonder, Demantra, or E2open, and internal capacity planning and supply-matching tools.
Statistical or machine-learning forecasting depth, including time-series, hierarchical or probabilistic forecasting, and forecast reconciliation.
PhD in Operations Research, Industrial Engineering, Management Science, or a related quantitative field.
Publications, patents, or recognized technical leadership in operations research, optimization, or forecasting.

Responsibilities

Own multi-horizon server and megawatt demand forecasts over the long range (2–5+ years), by resource type and region, and determine the data center supply requirements they drive.
Aggregate and normalize demand signals from short-term demand and product groups into a trusted statistical long-term demand plan.
Formulate and solve demand-supply matching problems using operations-research models that reconcile forecast demand with hardware roadmaps, cooling, lead times, and power constraints to inform the Plan of Record.
Build across the modeling spectrum, including production-grade optimization and forecasting systems as well as prototype models and heuristics to answer leadership questions or unblock decisions.
Set functional standards by defining the operating model, bridging short- and long-term forecasts, tracking accuracy and supply-matching metrics, and closing forecast-to-actual gaps.
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.
Set multi-year demand-planning and supply-matching strategy across Infrastructure organizations, identify capacity risks and opportunities years ahead, and guide decisions before they are expensive to reverse.
Mentor individual contributors across the planning organization and raise the modeling bar company-wide.