Modeling Analyst | 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 implement numerical methods for differential equations, linear and nonlinear systems, optimization, and simulation.
• Develop algorithms for integration, interpolation, approximation, differentiation, and root-finding; evaluate convergence, stability, accuracy, complexity, and reliability.

Simulation, validation, and optimization

• Simulate system behavior across parameters and operating scenarios; conduct sensitivity and uncertainty analyses.
• Validate models using analytical solutions, experiments, benchmark datasets, or observed behavior; identify numerical errors, instability, ill-conditioning, and accuracy issues.
• Develop optimization models and improve computation time, memory use, and performance; benchmark, profile, and test methods.

Implementation and collaboration

• Implement models and algorithms using Python, MATLAB, Julia, C++, or other suitable languages and scientific computing tools.
• Analyze simulation and experimental datasets; develop reusable code, modules, and automated workflows.
• Document assumptions, derivations, algorithms, procedures, and validation; prepare reports and presentations and collaborate across technical teams.
• Review existing models and support digital twins, predictive simulations, and model-based engineering where applicable.

Education and experience

• 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.

Technical qualifications

• Strong foundation in calculus, linear algebra, differential equations, numerical analysis, and optimization.
• Experience developing computational models and implementing theoretical formulations; proficiency in at least one of Python, MATLAB, Julia, C++, or Fortran.
• Familiarity with scientific computing libraries such as NumPy, SciPy, MATLAB toolboxes, or comparable platforms.
• Able to design, test, debug, and optimize algorithms; understands numerical stability, convergence, discretization error, floating-point arithmetic, and computational accuracy.
• Experience validating results using analytical comparisons, benchmarks, or independent computational methods.

Additional qualifications

• Optimization, sensitivity analysis, parameter estimation, statistical methods, probability, and uncertainty quantification are beneficial or advantageous.
• Simulation tools, finite element/difference/volume methods, and high-performance, parallel, GPU, or distributed computing are preferred or advantageous.
• Strong analytical reasoning, attention to detail, communication, technical documentation, collaboration, and commitment to mathematical rigor and reproducibility are expected.
• 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; the salary amount and cadence are not specified.
• Professional development, technical collaboration, scientific software, computing resources, and opportunities to contribute to research, simulation, and engineering optimization.