Operations Research / Systems Analyst (ORSA) - Optimization & Mathematical Programming

SMX

9h•$123,000 - $206,000

About The Position

The Operations Research (OR) Analyst - Optimization & Mathematical Programming Focus provides advanced optimization, resource allocation, and prescriptive analytics across a Federal Agency's personnel vetting, industrial security, and counterintelligence operations. This position formulates and solves complex mathematical programming problems that enable the Federal Agency to optimize resource deployment, prioritize competing requirements, and transition from reactive decision-making toward mathematically grounded, optimal resource allocation strategies.

Requirements

8+ years of progressive, hands-on operations research experience, including demonstrated application of mathematical optimization, resource allocation modeling, and prescriptive analytics to real-world operational problems
3–5 years of that experience supporting DoD or Intelligence Community mission areas such as personnel vetting, industrial security (NISP), counterintelligence, or insider threat
Expert-level proficiency in mathematical optimization including linear programming, integer programming, and mixed-integer programming
Hands-on experience with optimization solvers (Gurobi, CPLEX, FICO Xpress, or open-source alternatives such as Pyomo, PuLP, OR-Tools, COIN-OR)
Demonstrated ability to formulate real-world problems as mathematical programs, including objective function design and constraint identification
Proven proficiency in an analytical programming language (Python or R), with emphasis on optimization modeling libraries
Experience with constraint programming and heuristic solution methods for large-scale or computationally difficult problems
Strong foundation in algorithm design, computational complexity, and solution methods
Ability to validate optimization models using operational data and communicate results to non-technical stakeholders
Experience working in secure (classified) environments
Secret clearance required (active or ability to obtain)

Nice To Haves

Advanced degree in Operations Research, Applied Mathematics, Industrial Engineering, Management Science, or a related quantitative discipline
Familiarity with NISP, clearance adjudication processes, and/or insider threat/counterintelligence analytic frameworks
Experience with nonlinear optimization and stochastic optimization techniques
Knowledge of multi-objective optimization and Pareto analysis
Network optimization and graph algorithms (shortest path, max flow, matching problems)
Experience with scheduling and routing problems (job shop scheduling, vehicle routing)
Familiarity with game theory and decision analysis under uncertainty
Knowledge of operations research software (AMPL, GAMS)
Data visualization tools (Tableau, Power BI) for communicating optimization results
SQL and database querying skills to support model parameterization
Knowledge of queueing theory and simulation to better integrate with modeling specialists
Experience with statistical modeling and risk analysis to inform optimization formulations

Responsibilities

Formulate real-world resource allocation problems as mathematical optimization models (linear programming, integer programming, mixed-integer programming)
Develop and implement optimization algorithms for complex assignment problems including adjudicator workload distribution, facility inspection scheduling, and investigator allocation
Apply constraint programming and heuristic methods to solve large-scale, computationally challenging optimization problems
Build decision support tools that enable operational leaders to explore tradeoffs and make resource deployment decisions with mathematical rigor
Conduct cost-benefit analyses and apply mathematical programming techniques to optimize the distribution of personnel, budgetary, and operational resources across competing requirements
Apply risk-based optimization to prioritize facility assessments, case assignments, and inspection schedules based on threat levels and resource constraints
Perform trade-space analysis and sensitivity studies to understand how optimal solutions change under different assumptions, constraints, or objectives
Develop multi-objective optimization approaches that balance competing goals (speed, quality, cost, risk mitigation)
Utilize optimization solvers (Gurobi, CPLEX, or open-source alternatives like Pyomo, PuLP, OR-Tools) to implement and solve mathematical models
Validate optimization model outputs against historical operational data and subject matter expert judgment
Develop prescriptive analytics that recommend specific actions based on optimization results
Create scenario planning tools that allow decision-makers to explore "what-if" questions regarding resource allocation strategies
Work closely with modeling/simulation specialists to incorporate predictive analytics into optimization formulations
Partner with data engineering specialists to obtain empirically-grounded parameters, constraints, and objective function coefficients
Translate mathematical optimization results into clear, actionable recommendations for non-technical decision-makers
Present optimization approaches, tradeoff analyses, and recommendations to senior leadership
Participate in cross-functional team activities to maintain technical standards and share knowledge