{"schemaVersion":"jobsearcher.job.v1","id":"f35f1e31f7149486aa1cba8e","url":"https://jobsearcher.com/jobs/f35f1e31f7149486aa1cba8e","canonicalUrl":"https://jobsearcher.com/jobs/f35f1e31f7149486aa1cba8e","title":"Operations Research / Systems Analyst (ORSA) - Optimization & Mathematical Programming","description":"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.\n\nEssential Duties and Responsibilities\nMathematical Optimization & Resource Allocation\n\nFormulate real‑world resource allocation problems as mathematical optimization models (linear programming, integer programming, mixed‑integer programming)\n\nDevelop and implement optimization algorithms for complex assignment problems including adjudicator workload distribution, facility inspection scheduling, and investigator allocation\n\nApply constraint programming and heuristic methods to solve large‑scale, computationally challenging optimization problems\n\nBuild decision support tools that enable operational leaders to explore tradeoffs and make resource deployment decisions with mathematical rigor\n\nStrategic Analysis & Decision Support\n\nConduct cost‑benefit analyses and apply mathematical programming techniques to optimize the distribution of personnel, budgetary, and operational resources across competing requirements\n\nApply risk‑based optimization to prioritize facility assessments, case assignments, and inspection schedules based on threat levels and resource constraints\n\nPerform trade‑space analysis and sensitivity studies to understand how optimal solutions change under different assumptions, constraints, or objectives\n\nDevelop multi‑objective optimization approaches that balance competing goals (speed, quality, cost, risk mitigation)\n\nModel Development & Implementation\n\nUtilize optimization solvers (Gurobi, CPLEX, or open‑source alternatives like Pyomo, PuLP, OR‑Tools) to implement and solve mathematical models\n\nValidate optimization model outputs against historical operational data and subject matter expert judgment\n\nDevelop prescriptive analytics that recommend specific actions based on optimization results\n\nCreate scenario planning tools that allow decision‑makers to explore \"what‑if\" questions regarding resource allocation strategies\n\nCollaboration & Communication\n\nWork closely with modeling/simulation specialists to incorporate predictive analytics into optimization formulations\n\nPartner with data engineering specialists to obtain empirically‑grounded parameters, constraints, and objective function coefficients\n\nTranslate mathematical optimization results into clear, actionable recommendations for non‑technical decision‑makers\n\nPresent optimization approaches, tradeoff analyses, and recommendations to senior leadership\n\nParticipate in cross‑functional team activities to maintain technical standards and share knowledge\n\nRequired Skills & Experience\n\n8+ 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\n\n3–5 years of that experience supporting DoD or Intelligence Community mission areas such as personnel vetting, industrial security (NISP), counterintelligence, or insider threat\n\nExpert‑level proficiency in mathematical optimization including linear programming, integer programming, and mixed‑integer programming\n\nHands‑on experience with optimization solvers (Gurobi, CPLEX, FICO Xpress, or open‑source alternatives such as Pyomo, PuLP, OR‑Tools, COIN‑OR)\n\nDemonstrated ability to formulate real‑world problems as mathematical programs, including objective function design and constraint identification\n\nProven proficiency in an analytical programming language (Python or R), with emphasis on optimization modeling libraries\n\nExperience with constraint programming and heuristic solution methods for large‑scale or computationally difficult problems\n\nStrong foundation in algorithm design, computational complexity, and solution methods\n\nAbility to validate optimization models using operational data and communicate results to non‑technical stakeholders\n\nExperience working in secure (classified) government environments\n\nSecret clearance required (active or ability to obtain)\n\nDesired Skills & Experience\n\nAdvanced degree in Operations Research, Applied Mathematics, Industrial Engineering, Management Science, or a related quantitative discipline\n\nFamiliarity with NISP, clearance adjudication processes, and/or insider threat/counterintelligence analytic frameworks\n\nExperience with nonlinear optimization and stochastic optimization techniques\n\nKnowledge of multi‑objective optimization and Pareto analysis\n\nNetwork optimization and graph algorithms (shortest path, max flow, matching problems)\n\nExperience with scheduling and routing problems (job shop scheduling, vehicle routing)\n\nFamiliarity with game theory and decision analysis under uncertainty\n\nKnowledge of operations research software (AMPL, GAMS)\n\nData visualization tools (Tableau, Power BI) for communicating optimization results\n\nSQL and database querying skills to support model parameterization\n\nKnowledge of queueing theory and simulation to better integrate with modeling specialists\n\nExperience with statistical modeling and risk analysis to inform optimization formulations\n\nApplication Deadline\nJuly31,2026\n\nSalary: $123,000 – $206,000 USD\n\nSMX is an Equal Opportunity employer including disabilities and veterans.\n\nSelected applicant may be subject to a background investigation and/or education verification.\n\nSMX does not sponsor a new applicant for employment authorization or immigration related support for this position.\n\n#J-18808-Ljbffr","company":"SMX","rawCompany":"smx","city":"Pierre","state":"SD","isRemote":false,"isActive":false,"createdAt":"2026-07-06T03:25:46.712Z","occupations":[{"code":"15-2031.00","title":"Operations Research Analysts","slug":"operations-research-analysts"},{"code":"15-2099.00","title":"Mathematical Science Occupations, All Other","slug":"mathematical-science-occupations-all-other"},{"code":"15-2021.00","title":"Mathematicians","slug":"mathematicians"}],"industries":[{"code":"921190","title":"Other General Government Support","slug":"other-general-government-support"},{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"},{"code":"928110","title":"National Security","slug":"national-security"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Operations Research / Systems Analyst (ORSA) - Optimization & Mathematical Programming","description":"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.\n\nEssential Duties and Responsibilities\nMathematical Optimization & Resource Allocation\n\nFormulate real‑world resource allocation problems as mathematical optimization models (linear programming, integer programming, mixed‑integer programming)\n\nDevelop and implement optimization algorithms for complex assignment problems including adjudicator workload distribution, facility inspection scheduling, and investigator allocation\n\nApply constraint programming and heuristic methods to solve large‑scale, computationally challenging optimization problems\n\nBuild decision support tools that enable operational leaders to explore tradeoffs and make resource deployment decisions with mathematical rigor\n\nStrategic Analysis & Decision Support\n\nConduct cost‑benefit analyses and apply mathematical programming techniques to optimize the distribution of personnel, budgetary, and operational resources across competing requirements\n\nApply risk‑based optimization to prioritize facility assessments, case assignments, and inspection schedules based on threat levels and resource constraints\n\nPerform trade‑space analysis and sensitivity studies to understand how optimal solutions change under different assumptions, constraints, or objectives\n\nDevelop multi‑objective optimization approaches that balance competing goals (speed, quality, cost, risk mitigation)\n\nModel Development & Implementation\n\nUtilize optimization solvers (Gurobi, CPLEX, or open‑source alternatives like Pyomo, PuLP, OR‑Tools) to implement and solve mathematical models\n\nValidate optimization model outputs against historical operational data and subject matter expert judgment\n\nDevelop prescriptive analytics that recommend specific actions based on optimization results\n\nCreate scenario planning tools that allow decision‑makers to explore \"what‑if\" questions regarding resource allocation strategies\n\nCollaboration & Communication\n\nWork closely with modeling/simulation specialists to incorporate predictive analytics into optimization formulations\n\nPartner with data engineering specialists to obtain empirically‑grounded parameters, constraints, and objective function coefficients\n\nTranslate mathematical optimization results into clear, actionable recommendations for non‑technical decision‑makers\n\nPresent optimization approaches, tradeoff analyses, and recommendations to senior leadership\n\nParticipate in cross‑functional team activities to maintain technical standards and share knowledge\n\nRequired Skills & Experience\n\n8+ 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\n\n3–5 years of that experience supporting DoD or Intelligence Community mission areas such as personnel vetting, industrial security (NISP), counterintelligence, or insider threat\n\nExpert‑level proficiency in mathematical optimization including linear programming, integer programming, and mixed‑integer programming\n\nHands‑on experience with optimization solvers (Gurobi, CPLEX, FICO Xpress, or open‑source alternatives such as Pyomo, PuLP, OR‑Tools, COIN‑OR)\n\nDemonstrated ability to formulate real‑world problems as mathematical programs, including objective function design and constraint identification\n\nProven proficiency in an analytical programming language (Python or R), with emphasis on optimization modeling libraries\n\nExperience with constraint programming and heuristic solution methods for large‑scale or computationally difficult problems\n\nStrong foundation in algorithm design, computational complexity, and solution methods\n\nAbility to validate optimization models using operational data and communicate results to non‑technical stakeholders\n\nExperience working in secure (classified) government environments\n\nSecret clearance required (active or ability to obtain)\n\nDesired Skills & Experience\n\nAdvanced degree in Operations Research, Applied Mathematics, Industrial Engineering, Management Science, or a related quantitative discipline\n\nFamiliarity with NISP, clearance adjudication processes, and/or insider threat/counterintelligence analytic frameworks\n\nExperience with nonlinear optimization and stochastic optimization techniques\n\nKnowledge of multi‑objective optimization and Pareto analysis\n\nNetwork optimization and graph algorithms (shortest path, max flow, matching problems)\n\nExperience with scheduling and routing problems (job shop scheduling, vehicle routing)\n\nFamiliarity with game theory and decision analysis under uncertainty\n\nKnowledge of operations research software (AMPL, GAMS)\n\nData visualization tools (Tableau, Power BI) for communicating optimization results\n\nSQL and database querying skills to support model parameterization\n\nKnowledge of queueing theory and simulation to better integrate with modeling specialists\n\nExperience with statistical modeling and risk analysis to inform optimization formulations\n\nApplication Deadline\nJuly31,2026\n\nSalary: $123,000 – $206,000 USD\n\nSMX is an Equal Opportunity employer including disabilities and veterans.\n\nSelected applicant may be subject to a background investigation and/or education verification.\n\nSMX does not sponsor a new applicant for employment authorization or immigration related support for this position.\n\n#J-18808-Ljbffr","datePosted":"2026-07-06T03:25:46.712Z","dateModified":"2026-07-06T03:25:46.712Z","hiringOrganization":{"@type":"Organization","name":"SMX","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Pierre","addressRegion":"SD","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"f35f1e31f7149486aa1cba8e"},"url":"https://jobsearcher.com/jobs/f35f1e31f7149486aa1cba8e"}}