Applied Math Jobs in Minnesota (NOW HIRING)

Join the world's largest HVAC company, named by Forbes as one of America's Best-In-State Employers 2025! Staff Development Engineer IV – Engineering Data Scientist & Digital Twin Specialist– Plymouth, MN - Hybrid Daikin Applied is seeking an Engineering Data Scientist & Digital Twin Specialist with a strong focus on Reduced Order Modeling (ROM). In this role, you will bridge the gap between high-fidelity 3D physic-based (FEA/CFD), 1D system performance simulations, lab, and real-time operational data. You will build, validate, and deploy fast-running surrogate models and hybrid digital twins that power predictive maintenance, real-time edge analytics, and automated design optimization for our physical assets and systems. Come be a part of an exciting journey at Daikin Applied, where innovation and excellence drive our every endeavor!

• Reduced Order Modeling (ROM): Develop, calibrate, and validate ROMs from complex 3D/multiphysics simulations (e.g., thermal, structural, fluid dynamics) to accelerate computation speeds by orders of magnitude without losing fidelity.
• Hybrid Digital Twin Development: Design and implement hybrid digital twins that combine first-principles physical models with machine learning/AI (physics-informed neural networks, surrogate modeling) to mirror real-world asset behavior.
• Data Integration & Pipelines: Ingest, clean, and utilize high-frequency time-series telemetry and IoT sensor data from physical machinery/assets to continuously update and retrain digital models.
• Deployment & Scaling: Package and deploy ROMs into production environments, cloud platforms, or real-time edge devices using platforms like Ansys Twin Builder, Siemens Simcenter, or custom Python/C++ frameworks.
• Cross-Functional Collaboration: Work tightly with domain engineers, software developers, and data engineers to integrate digital twin frameworks into broader enterprise architectures and PLM.
• Model Validation: Conduct rigorous regression testing, scenario analysis, and test-data correlation to ensure numerical stability and accuracy against physical counterparts.

• Master’s or Ph.D. in Mechanical Engineering, Aerospace Engineering, Computer Science, Applied Mathematics, Data Science, or a related technical discipline
• 6+ years of industry/research experience in applied machine learning, scientific computing, or physics-based simulation
• Proven track record of building and deploying Reduced Order Models (ROMs) (e.g., Proper Orthogonal Decomposition (POD), Dynamic Mode Decomposition (DMD), or machine learning surrogates like Gaussian Processes and neural networks)
• Advanced proficiency in Python (NumPy, PyTorch/TensorFlow, Scikit-learn) and/or C++
• Familiarity with engineering simulation software suites (e.g., Ansys Twin Builder, Siemens Simcenter, MATLAB/Simulink, or OpenFOAM/FEA tools)
• Experience with time-series databases, IoT data streams (MQTT, OPC UA), and containerization (Docker, Kubernetes) for model deployment
• Strong understanding of physical principles (dynamics, thermodynamics, heat transfer, structures, or fluid mechanics) alongside statistical modeling and machine learning
• Strong communication and presentation skills, with the ability to clearly convey technical concepts to both technical and non-technical audiences
• Demonstrated ability to lead technical project teams and mentor engineers
• Knowledge of systems engineering and architecture principles
• Demonstrated ability to work independently and drive collaboration in a cross-functional, globally distributed environment
• Understanding of model reuse, simulation governance, and lifecycle management concepts
• Track record of leading cross-disciplinary simulation initiatives or shaping organizational modeling strategy

• Experience with MiL and HiL simulation workflows
• Experience with machine learning, data analytics, or AI-assisted modeling and automation
• Background in experimental data acquisition and validation of simulation models using test data
• Experience with physics-informed machine learning (PINMs) or geometric deep learning
• Exposure to industrial IoT platforms or 3D real-time visualization frameworks (NVIDIA Omniverse, Unity/Unreal)
• Knowledge of Model-Based Systems Engineering (MBSE) methodologies
• Deep understanding of thermodynamic cycle modeling, HVAC&R systems, fluid mechanics, heat transfer fundamentals, oil circulation effects, and both steady-state and dynamic system behavior
• Extensive experience developing, calibrating, and troubleshooting complex model libraries, parameter databases, and calibration routines

• Daikin Applied offers the following benefits for this position, subject to applicable eligibility requirements:
• Multiple medical insurance plan options + dental and vision insurance
• 401K retirement plan with employer match
• Paid time off and company paid holidays
• Paid sick time in accordance with the federal, state and local law
• Tuition Reimbursement after 6 months of continuous service
• Work visa sponsorship is not available for this position
• The typical annual base salary for this position ranges from $109,100 - $118,700 plus a 15% bonus in Minnesota. The range displayed represents the pay range for all positions in the job grade which these positions fall. Individual base pay will depend on a wide range of factors including your skills, qualifications, experience, and location.
• #LI-DF1

Daikin Applied is not just an HVAC company. We're part of a global technology powerhouse that uses Heating, Ventilation and Air Conditioning (HVAC) to transform the world. We're innovators and leaders. Not only as a business, but as individuals. And we are honored to say we were named to Forbes' America's Best-In-State Employers list for 2025, ranking #1 in Minnesota for Manufacturing and Engineering. You see, we're not just innovating on the outside. We're innovating on the inside to unlock people's unlimited potential and bring out the diverse and groundbreaking ideas that will help us solve today's most pressing challenges. That's why Daikin offers more than a job.