Research Scientist - Demand Forecasting
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Before you apply
Salary$154k – $217k
LocationMenlo Park, CA
Typefull-time
SenioritySenior
Experience6+ yrs
DepartmentInfrastructure
EquityIncluded
Company size10,000+ people
PostedSep 6, 2026 · 1mo ago
Verified live17h ago
At a glanceSummarised by Seekless from the posting.
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)
Fluency 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
Skills
SQL
Python
Gurobi
CPLEX
Xpress
OR-Tools
Kinaxis
SAP IBP
o9
Blue Yonder
Demantra
E2open
About the role
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.
Responsibilities
•
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
Minimum 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
Preferred qualifications
•
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