Modeling Analyst | Numerical Methods Engineer

Role overview

Modeling and numerical methods

• Model physical systems, engineering processes, scientific phenomena, and operational problems; translate requirements into mathematical formulations and computational strategies.
• Design and apply numerical methods for differential equations, linear and nonlinear systems, optimization, eigenvalue calculations, and simulations.
• Develop algorithms for integration, interpolation, approximation, differentiation, and root-finding; evaluate accuracy, stability, convergence, and computational complexity.
• Simulate system behavior across conditions and parameters; perform sensitivity and uncertainty analyses.
• Validate models against analytical solutions, experimental measurements, benchmarks, or observed behavior; identify numerical errors and instability.
• Improve computation time, memory use, and performance; benchmark, profile, and systematically test algorithms.
• Implement models with Python, MATLAB, Julia, C++, or other suitable languages and scientific-computing libraries.
• Apply linear algebra, calculus, differential equations, probability, statistics, and optimization; analyze multidimensional simulation and experimental datasets.
• Design computational experiments; create reusable code, modules, and automated workflows; support digital twins and model-based engineering where applicable.
• Document assumptions, derivations, algorithms, procedures, and validation; prepare reports, research summaries, results, and presentations.
• Collaborate across disciplines, review existing models, participate in technical reviews, and recommend methodological improvements.
• Maintain version-controlled code and reproducible environments; keep informed about advances in numerical analysis and scientific computing.

Required qualifications

• Bachelor's or master's degree in applied or 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 foundation in calculus, linear algebra, differential equations, numerical analysis, and mathematical optimization.
• Understand numerical techniques for linear systems, nonlinear equations, differential equations, numerical stability, convergence, discretization error, floating-point arithmetic, and computational accuracy.
• Experience developing mathematical models and translating theoretical formulations into computational implementations.
• Proficiency in at least one scientific-computing language, such as Python, MATLAB, Julia, C++, or Fortran.
• Familiarity with numerical or scientific-computing libraries such as NumPy, SciPy, MATLAB toolboxes, or comparable platforms.
• Ability to design, test, debug, optimize, and validate computational algorithms and numerical results.

Advantageous and preferred qualifications

• Knowledge of optimization, sensitivity analysis, and parameter estimation is advantageous; statistical methods, probability, and uncertainty quantification are beneficial.
• Simulation tools relevant to engineering or scientific applications are preferred; finite element, finite difference, or finite volume methods are advantageous for engineering-focused roles.
• High-performance computing, parallel algorithms, GPU computing, or distributed numerical computation is beneficial.
• Git or other version control and reproducible research practices are preferred.
• A master's or doctoral degree, or relevant research experience, is advantageous for advanced modeling and research positions.
• Strong analytical reasoning, attention to detail, clear technical communication, documentation experience, collaboration, and the ability to manage multiple tasks and deadlines are sought.

What the employer offers

• Competitive salary and comprehensive employee benefits; no salary amount or payment cadence is stated.
• Professional development, technical training, advanced-study support, scientific software, computing resources, and technical learning materials.
• Opportunities to work on challenging research, simulation, optimization, and computational modeling projects with multidisciplinary specialists.
• Career development paths include senior modeling, numerical simulation, computational science, applied mathematics, and computational engineering roles.