Staff Development Engineer IV - Engineering Data Scientist and Digital Twin Specialist

Daikin Applied Americas•Plymouth, MN

2d•$109,100 - $118,700•Hybrid

About The Position

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!

Requirements

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

Nice To Haves

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

Responsibilities

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.
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.
Ingest, clean, and utilize high-frequency time-series telemetry and IoT sensor data from physical machinery/assets to continuously update and retrain digital models.
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.
Work tightly with domain engineers, software developers, and data engineers to integrate digital twin frameworks into broader enterprise architectures and PLM.
Conduct rigorous regression testing, scenario analysis, and test-data correlation to ensure numerical stability and accuracy against physical counterparts.