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The Work: Join a growing analytics practice applying mathematical optimization to real-world resource-allocation challenges for federal clients. You'll formulate and solve constrained optimization problems that complement statistical and AI-driven modeling, helping identify the single best course of action from a complex set of constraints.
Key Responsibilities:
  • Formulate real-world resource-allocation and scheduling problems as linear or mixed-integer optimization models.
  • Build and maintain optimization solvers integrated into a broader analytics platform.
  • Define and validate constraint sets in partnership with subject matter experts, and review results with stakeholders to confirm outputs reflect real-world operational constraints.
  • Benchmark optimization outputs against statistical and AI-based modeling approaches for cross-validation.
  • Here Is What You NeedMust be a US Citizen
  • Must have an active Secret clearance
Bachelor's degree in Statistics, Data Science, Operations Research, Industrial Engineering, Applied Mathematics, or related field (or equivalent practical experience - e.g., an additional 4 years of relevant hands-on experience in lieu of a degree).4+ years of professional experience in linear programming, mixed-integer programming, or constraint-based optimization (e.g., using solvers such as CPLEX, Gurobi, OR-Tools, or similar).Proficiency in Python for integrating optimization models into production systems. Ability to work full time on-site in the DC area. Preferred Experience Background in operations research applied to defense, healthcare, or logistics /scheduling domains. Familiarity with statistical simulation or agent-based modeling. Experience with Palantir Foundry (including its native optimization/decision tooling).The pay range for the states of California, Colorado, Connecticut, Hawaii, Illinois, Maine, Maryland, Massachusetts, Minnesota, New Jersey, New York, Ohio, Vermont, Virginia, Washington, and the District of Columbia is: $89,000 —$170,200 USD

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