Refrigeration Systems Modeling Engineer

Key Responsibilities :

Develop and maintain accurate refrigerant component / system models, ensuring

alignment with product requirements.

Participate in the full lifecycle of refrigerant component calculation development in C#

or Python, including specification, design, implementation, unit testing, accuracy

assurance, integration with .NET APIs, and maintenance.

Design, develop, and validate complex thermal models for refrigerant components, such

as heat exchangers, chillers, compressors, condensers, evaporators, and expansion

valves.

Apply Model-Based Design (MBD) methodologies to develop, implement, and verify

real-time control systems for refrigeration systems and refrigerated display cabinets.

Interpret simulation results to extract insights and inform design decisions.

Collaborate with Product Owners and technical teams to troubleshoot and resolve

issues.

Conduct model testing, calibration, and validation using experimental data.

Work with global engineering design centers on challenging modeling problems,

promoting the use of simulation techniques to support design decisions.

Demonstrate the ability to learn new modeling tools / inhouse tools quickly and apply

them effectively to solve complex engineering problems.

Qualifications :

o MS in Engineering, Data Analytics, or Physics, OR

o Ph.D. in Mechanical Engineering with specialization in Thermal and Fluid Science.

Experience :

o 8+ years of relevant experience in thermal modeling or related fields.

Mandatory Skills :

o Proficiency in C# / Python for model development.

o Strong understanding of HVAC&R systems, thermal fluid systems, and

thermodynamic cycles.

o Expertise in writing / solving equation-based models

o Knowledge of refrigerant components such as heat exchangers, chillers,

compressors, condensers, and evaporators.

Additional Skills (Preferred) :

o Experience in Python, .NET, Fortran, SQL,

o Hands-on experience with tools like ANSYS Fluent, MATLAB / Simulink, and

Dymola.

o Familiarity with machine learning, signal processing, and optimization

techniques.

o Experience in model-based systems engineering (MBSE) methodologies.

o Knowledge of basic refrigerant cycles (e.G., single-stage compression cycle).

o Experience with embedded systems and lab testing.