Director, AI & Process Solutions
$184,000–$245,500 year
RemotePennsylvania, United States
Job Summary
Define the AI, machine learning, and generative AI strategy across Materials Division R&D, including roadmap development for scientific copilots, autonomous workflows, and SBU-specific language models. Lead deployment of intelligent assistants and digital twins to accelerate product development, scale-up, and data-driven decision making from research through manufacturing. Partner with scientists, engineers, and business leaders to identify high-value opportunities, build reusable knowledge assets, and transition mature models into production environments. Establish AI governance, risk management, and responsible AI practices while driving adoption through stakeholder engagement and change management. Build and lead a high-performing team of AI/ML and digital specialists to foster continuous innovation.
Required Qualifications
- M.S. or Ph.D. in Computer Science, Data Science, Artificial Intelligence, Chemistry, Materials Science, Chemical Engineering, or related technical discipline
- 10+ years of progressive experience in AI/ML, advanced analytics, computational science, process modeling, digital engineering, scientific computing, or digital innovation in R&D-intensive or manufacturing environments
- Demonstrated experience leading multidisciplinary technical teams and delivering scalable AI, analytics, modeling, or digital process solutions across multiple business or functional groups
- Strong understanding of machine learning, generative AI, large language models, scientific language models, agentic workflows, retrieval-augmented generation, knowledge graphs, semantic search, and modern AI architectures
- Working knowledge of process modeling, simulation, digital twins, analytical measurement, process characterization, scientific workflows, data integration, and model lifecycle practices
- Experience translating business and scientific challenges into practical AI-enabled and data-driven solutions with measurable outcomes
- Experience applying AI, advanced analytics, or modeling solutions to product development, process development, technology transfer, scale-up, manufacturing support, quality, or industrial R&D environments
- Strong understanding of AI governance, responsible AI practices, model validation, cybersecurity considerations, documentation, and risk management
- Strong executive communication, stakeholder management, and influencing skills with the ability to communicate complex technical strategies to scientific, engineering, functional, and business leadership audiences
Desired Qualifications
- Experience in semiconductor materials, specialty chemicals, advanced materials, formulations, analytical sciences, advanced manufacturing, or related industrial R&D environments is a plus
- Publications, patents, or significant internal contributions in AI, machine learning, process systems, scientific computing, digital engineering, or digital transformation
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