Digital Innovation Engineer

Predictive Modeling for Material Property Design

• Develop and apply predictive and hybrid machine learning approaches for the prediction of properties key to designing the next generation of materials.
• Integrate mechanistic understanding, statistical modeling, and data-driven methods to generate reliable, decision-ready predictions.
• Quantify model confidence and limitations to support risk-aware technical decisions.
• Translate complex modeling outputs into clear, actionable insights for technology and innovation stakeholders.

Experimental Design & Bayesian Optimization for New Product Development

• Design and apply advanced experimental design strategies and Bayesian optimization for new product development.
• Efficiently explore high-dimensional design spaces to prioritize experiments and identify optimal candidates for laboratory evaluation.
• Apply adaptive and sequential learning approaches to balance exploration and exploitation under limited data conditions.

• Master's Degree or higher, or with equivalent experience in computer science, computer engineering, machine learning, physics, applied mathematics or related field
• Understanding of advanced materials, chemical processes, and laboratory data is a plus.
• 1+ years' work experience with modeling development, data analysis, business communication, and digital transformation is highly desirable.
• Proficiency in AI + physics-based machine learning.
• Working understanding of material science fundamentals
• Strong foundation in applied statistics, experimental design, and probabilistic modeling.
• Expertise in predictive modeling and simulation for material or system property prediction.
• Experience with uncertainty quantification, model validation, and decision support under uncertainty.
• Ability to translate advanced quantitative methods into practical workflows including proof-of-concept full-stack (backend + frontend) applications that inform technology and product decisions.
• Working across the full lifecycle: problem formulation * model and strategy development * application and adoption.
• Communicating complex modeling and experimental concepts clearly to diverse technical audiences.
• Influencing technology and innovation decisions through quantitative, model-driven insight.
• Operating effectively in cross-functional environments spanning product development, technology, innovation, and digital teams.

Celanese is an Equal Opportunity Employer. Celanese does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, non-disqualifying physical or mental disability, national origin, veteran status or any other basis covered by appropriate law. All employment is decided on the basis of qualifications, merit, and business need.

Celanese is a global leader in chemistry, producing specialty material solutions used across most major industries and consumer applications. Our businesses use our chemistry, technology and commercial expertise to create value for our customers, employees and shareholders. We support sustainability by responsibly managing the materials we create and growing our portfolio of sustainable products to meet customer and societal demand. We strive to make a positive impact in our communities and to foster inclusivity across our teams. Celanese Corporation employs more than 11,000 employees worldwide with 2024 net sales of $10.3 billion. For more information about Celanese Corporation and its product offerings, visit www.celanese.com.