Applied Scientist - Global Operations Tech
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The role
Lead and translate complex optimization problems into practical decision-support tools for a fast-moving logistics network.
Develop and apply mathematical optimization methods (LP, IP, DP, network flows) and economic analyses to improve delivery times and cost efficiency.
Model, validate, and implement scalable solutions using Python, R, and industry solvers (Gurobi, CPLEX, Xpress).
Collaborate with cross-functional teams to present business cases, document analyses, and influence strategic decisions.
Navigate uncertainty and simulate large-scale systems to deliver near-optimal or optimal strategies for operations.
Requires 10+ years in operations research with extensive experience in supply chain, transportation, and logistics.
Develop and apply mathematical optimization methods (LP, IP, DP, network flows) and economic analyses to improve delivery times and cost efficiency.
Model, validate, and implement scalable solutions using Python, R, and industry solvers (Gurobi, CPLEX, Xpress).
Collaborate with cross-functional teams to present business cases, document analyses, and influence strategic decisions.
Navigate uncertainty and simulate large-scale systems to deliver near-optimal or optimal strategies for operations.
Requires 10+ years in operations research with extensive experience in supply chain, transportation, and logistics.
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