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

N2IA Technologies•Washington, DC

8d•Onsite

About The Position

N2IA Technologies is a consulting company specializing in acquisition/contracting support, cost/FinOps, and technology optimization for federal clients. We deliver tailored strategies, robust software solutions, and streamlined operations to help organizations achieve their goals. At N2IA, we are committed to developing innovative financial and compliance-based solutions that address evolving business and regulatory needs. If you are passionate about accounting, financial accuracy, and supporting government programs, N2IA is the place for you. The Operations Research / Systems Analyst (ORSA) - 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) government environments.
Secret clearance required (active or ability to obtain).
Bachelor degree in Operations Research, Applied Mathematics, Industrial Engineering, Management Science, or a related quantitative discipline.

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.