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About the Role

Hexcel is seeking a Research and Development Data Scientist to lead the development and deployment of digital twin technologies that accelerate material, process, and product innovation. This role focuses on building virtual representations of manufacturing processes, materials, and products using advanced analytics, machine learning, physics-based modeling, and AI, bridging physical experimentation and digital simulation.

Responsibilities

Lead the design, development, validation, and deployment of digital twins for manufacturing processes, material systems, and product development programs.
Develop hybrid modeling approaches combining physics-based models, first-principles engineering, machine learning, and statistical methodologies.
Integrate sensor, process, quality, and operational data into digital twin frameworks to improve predictive capabilities and decision-making.
Design and implement advanced AI, machine learning, and analytics solutions to enhance model accuracy, process control, and product performance.
Collaborate with scientists, engineers, technicians, and manufacturing teams to translate physical system behavior into scalable digital twin architectures.
Support experimentation strategies, including Design of Experiments (DOE), active learning, and closed-loop optimization to continuously improve digital twin performance.
Develop simulation environments for evaluating process changes, material innovations, and manufacturing outcomes before physical implementation.
Establish model governance practices including validation, uncertainty quantification, monitoring, and lifecycle management of digital twin applications.
Communicate digital twin insights and recommendations to technical and business stakeholders to drive data-driven innovation and operational excellence.

Requirements

Master's degree in data science, Materials Science, Chemical Engineering, Mechanical Engineering, Applied Mathematics, Physics, or a related technical discipline.
Minimum 2+ years of experience developing predictive models, digital twins, simulation tools, or advanced analytics solutions in an industrial environment.
Demonstrated experience creating and validating digital twin or virtual process models using machine learning, statistical modeling, physics-based modeling, or hybrid approaches.
Experience modeling complex physical, chemical, or manufacturing systems.
Strong analytical and problem-solving skills with the ability to connect physical processes to data-driven modeling techniques.
Proficiency in Python, R, or similar programming languages and associated machine learning ecosystems.
Experience with cloud-based data and AI platforms, preferably Microsoft Azure.
Strong communication skills with the ability to explain complex modeling concepts to multidisciplinary teams.
Eligible candidates must be a U.S. citizen.

Skills

Digital Twin platforms
Industrial IoT architectures
Digital thread initiatives
Azure Digital Twins
Azure Machine Learning
Azure IoT
Microsoft Fabric
Synapse
Manufacturing data
Process optimization
Materials development
Composite materials
ML Ops
Model deployment
Model governance
Continuous monitoring frameworks
Large language models (LLMs)
Generative AI
AI agents
AI and optimization techniques
Material formulation
Chemical composition development
Python
R
Machine learning
Statistical modeling
Physics-based modeling
Hybrid approaches
Cloud-based data and AI platforms
Microsoft Azure

Location

Salt Lake City, UT, USA

Work Type

Full-time

Experience Level

2+ years of experience

Education Level

Master's degree

About the Company

Hexcel is the industry leader in the manufacturing of advance composite materials, including carbon fiber, woven reinforcements, resins, prepregs, honeycombs and engineered core and composite structures.
Hexcel (NYSE: HXL) is a global leader in advanced composites technology, a leading producer of carbon fiber, and the world leader in honeycomb manufacturing for the commercial aerospace industry.

Equal Opportunity

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, physical or mental disability, status as a protected veteran, or any other protected class.