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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Engineering Data Scientist & Digital Twin Specialist - **Company:** Daikin Applied - **Location:** Plymouth, MN, United States - **Experience:** Expert - **Salary:** $109,100.0 - $118,700.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Systems Engineering, Artificial Neural Networks, C++ (Programming Language), Computational Fluid Dynamics, Databases, Data Integration, Dynamical Systems, Experimental Data, Python (Programming Language), MATLAB, Machine Learning, Microsoft Dynamics, Message Queuing Telemetry Transport (MQTT), NumPy, Regression Testing, Tensorflow, OPC Unified Architecture, Scientific Computating, Simulation Software, Test Data, Digital Twin, Pytorch, Deep Learning, Model Validation, Gaussian, Containerization, Scikit Learn, Kubernetes, Information Technology, Data Analytics, Ansys, C++ Frameworks, Docker - **Published:** August 22, 2026 - **Apply:** https://www.dice.com/job-detail/a6196898-a5da-46fa-9644-d7de6ff31588 ## About the Role * 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 Your Preferred Qualifications: * 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 ## Description * 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. ## Related Videos - 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