Mathematical Modeling Analyst | Numerical Methods Engineer

Role overview

Responsibilities

• Model physical systems, engineering processes, scientific phenomena, and operational problems; translate requirements into mathematical formulations and computational frameworks.
• Design and evaluate numerical methods for differential equations, linear and nonlinear systems, optimization, eigenvalue calculations, integration, interpolation, approximation, differentiation, and root-finding.
• Evaluate convergence, stability, accuracy, computational complexity, numerical error, and solution reliability; improve computational efficiency and performance.
• Build simulations; conduct sensitivity and uncertainty analyses; validate models against analytical solutions, experiments, benchmark datasets, or observed behavior.
• Implement models and algorithms with scientific programming languages and libraries; analyze simulation and experimental datasets and design computational experiments.
• Develop reusable code and automated workflows; document methods and findings; maintain version-controlled code and reproducible environments; collaborate on engineering and research applications.

Requirements

• Bachelor's or master's degree in applied mathematics, computational mathematics, numerical analysis, engineering, physics, scientific computing, or a related discipline.
• Relevant experience in mathematical modeling, numerical methods, scientific computing, simulation, or computational engineering.
• Strong foundations in calculus, linear algebra, differential equations, numerical analysis, and mathematical optimization.
• Experience implementing mathematical models and proficiency in at least one scientific-computing language: Python, MATLAB, Julia, C++, or Fortran.
• Familiarity with scientific computing libraries such as NumPy, SciPy, MATLAB toolboxes, or comparable platforms; ability to design, test, debug, and optimize algorithms.
• Understanding of stability, convergence, discretization error, floating-point arithmetic, and computational accuracy; experience validating results.
• Knowledge of optimization, sensitivity analysis, and parameter estimation is advantageous; statistical methods, probability, and uncertainty quantification are beneficial.
• Simulation tools, finite element/difference/volume methods, high-performance or parallel computing, and Git or reproducible research practices are preferred or advantageous.
• Strong analytical reasoning, problem-solving, attention to detail, clear technical communication, documentation, collaboration, and ability to manage multiple tasks and deadlines.
• A master's or doctoral degree or relevant research experience is advantageous for advanced modeling and research positions.

What the employer offers

• Competitive salary and comprehensive employee benefits.
• Professional development, technical training, advanced study, and exposure to scientific software, computing resources, simulation, optimization, and computational modeling.
• Opportunities to collaborate on research and engineering initiatives and progress toward senior modeling, simulation, computational science, applied mathematics, or computational engineering roles.