Hexcel Corporation

Hexcel Corporation

R&D Data Scientist

USA - UT - Salt Lake City Fibers · Contract

Sponsorship not specifiedDetected 6 days ago
PythonAlgorithmsAzureMachine LearningData EngineeringData ScienceProcess ImprovementResearch

About the role

  • We invite you to join the Hexcel team at various manufacturing sites, sales offices, and research and technology centers around the globe.
  • Hexcel is an Equal Opportunity Employer.
  • 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.

Responsibilities

  • Design and implement advanced data science and AI solutions to optimize process performance, improve product quality, and accelerate innovation.
  • Work in cross-functional teams of scientists, technicians, and engineers in product design and development programs.
  • Translate analytical findings into practical recommendations and support the implementation of data-driven improvements across cross-functional teams.

Requirements

  • Master's degree in data science
  • bachelor's degree in materials science, chemistry, chemical engineering, or related field.
  • Experience working closely with product development teams to translate technical insights into actionable improvements in process and product performance.
  • Proficiency in data preprocessing and data cleaning techniques, including handling large, complex, and noisy datasets.
  • Strong programming skills in languages such as Python or R, with experience using relevant data science techniques
  • Experience with Microsoft's technology stack and the Azure digital toolset
  • Minimum of 2 years of experience in industrial setting, preferably with demonstrated experience applying machine learning and advanced analytics to real-world problems.
  • Proven ability to develop and implement digital twin and other models for processes or systems using machine learning and traditional modeling techniques. Experience working with mathematical modeling of physical systems a plus.
  • Experience working closely with product development teams to translate technical insights into actionable improvements in process and product performance. Working knowledge of DOE (Design of Experiments) and other active learning methods.
  • Demonstrated ability to apply state-of-the-art methods in data science and AI to support process optimization, product innovation, and decision-making.
  • Eligible candidates must be a: U.S. citizen, U.S. national, person lawfully admitted for permanent residence, temporary resident under sections 210(a) or 245(A) of the Immigration and Nationality Act, person admitted in refugee status, or person granted asylum. 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.
  • Hexcel is an Equal Opportunity Employer. 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.

Nice to have

  • Experience working with mathematical modeling of physical systems a plus.
  • Experience with multiple large and small language models including models designed for optimized chemical compositions preferred.

Benefits

  • Develop, validate, and maintain digital twin models of key processes using machine learning and physics-informed or statistical approaches.
  • Supports design and experiments (DOE) and active learning strategies at lab, pilot and production scale.

Equal opportunity

  • 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.

Visa & Work Authorization

  • citizen, U

This listing is sourced directly from Hexcel Corporation's careers page and normalized into a canonical job model.