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Mathematical Engineer Jobs (NOW HIRING)

$63K/yr

OR COMBINATION OF EDUCATION AND EXPERIENCE: at least 24 hours of mathematics and statistics ... Experience in administrative aspects of task engineering and management, e.g., procurement policies ...

$63K/yr

OR COMBINATION OF EDUCATION AND EXPERIENCE: at least 24 hours of mathematics and statistics ... Experience in administrative aspects of task engineering and management, e.g., procurement policies ...

$63K/yr

OR COMBINATION OF EDUCATION AND EXPERIENCE: at least 24 hours of mathematics and statistics ... Experience in administrative aspects of task engineering and management, e.g., procurement policies ...

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Mathematical Engineer information

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$36.5K

$107.3K

$137.5K

How much do mathematical engineer jobs pay per year?

As of Aug 22, 2026, the average yearly pay for mathematical engineer in the United States is $107,282.00, according to ZipRecruiter salary data. Most workers in this role earn between $88,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is a mathematical engineer?

A Mathematical Engineer applies advanced mathematical concepts, computational techniques, and algorithms to solve complex engineering and technical problems. They work in various industries such as finance, data science, cryptography, and simulation modeling. Their role involves designing mathematical models, optimizing systems, and analyzing large datasets to improve efficiency and decision-making. They often collaborate with engineers, scientists, and analysts to develop innovative solutions for real-world challenges.

What are the key skills and qualifications needed to thrive as a mathematical engineer?

To thrive as a Mathematical Engineer, you need a strong background in advanced mathematics, mathematical modeling, and engineering principles, usually supported by a degree in mathematics, engineering, or a related field. Proficiency with programming languages such as MATLAB, Python, or R, and familiarity with simulation or modeling software is often expected. Exceptional problem-solving abilities, effective communication, and teamwork skills are highly valued in this interdisciplinary role. These competencies enable Mathematical Engineers to develop robust solutions for complex engineering challenges and collaborate successfully with diverse technical teams.

What are common challenges faced by mathematical engineers in their daily work?

Mathematical Engineers often tackle challenges such as translating real-world engineering problems into precise mathematical models and ensuring those models accurately represent system behaviors. Working with large datasets, complex algorithms, or advanced simulations requires strong analytical skills and attention to detail. Close collaboration with other engineers, researchers, and technical experts is common, so balancing technical depth with clear communication can be demanding but rewarding. Overcoming these challenges helps Mathematical Engineers deliver impactful, data-driven solutions in industries like aerospace, finance, or manufacturing.

What does a mathematical engineer do?

A mathematical engineer applies advanced mathematical techniques and models to solve complex problems in engineering, science, and technology. They often work with data analysis, simulations, and algorithm development using tools like MATLAB or Python, and may be involved in research, product development, or optimization tasks.
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Cities with the most Mathematical Engineer job openings:

What are the most commonly searched types of Mathematical Engineer jobs?

The most popular types of Mathematical Engineer jobs are:

What states have the most Mathematical Engineer jobs?

States with the most job openings for Mathematical Engineer jobs include:

Infographic showing various Mathematical Engineer job openings in the United States as of August 2026, with employment types broken down into 94% Full Time, 2% Part Time, and 4% Contract. Highlights an 85% Physical, 6% Hybrid, and 9% Remote job distribution, with an average salary of $107,282 per year, or $51.6 per hour.

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

N2IA Technologies

Washington, DC โ€ข On-site

$165K - $195K/yr

Full-time

Posted 24 days ago


Job description

Operations Research / Systems Analyst (ORSA) - Optimization & Mathematical Programming Focus Our CompanyN2IA 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.Position OverviewThe 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.Key Responsibilities:Mathematical Optimization & Resource AllocationFormulate 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 rigorStrategic Analysis & Decision SupportConduct 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)Model Development & ImplementationUtilize 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 strategiesCollaboration & CommunicationWork 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.Required Qualifications8+ 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.Preferred QualificationsAdvanced 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.N2IA is committed to fostering a diverse and inclusive work environment. We are an Equal Employment Opportunity Employer and encourage applications from all qualified individuals, regardless of gender, race, ethnicity, sexual orientation, disability, or veteran status.