Senior Scientist, Deep Learning Engineer Data- Experimentation; DRE

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In this role, the chosen candidate will work with a team of scientists tasked with identifying, developing, and deploying data‑rich technologies aimed at improving the manner in which process understanding is gathered. The tools we develop are as diverse as the team itself, and in this Senior Scientist role, the candidate will leverage fundamental process modeling, machine learning, and other innovative data science approaches to enable our process research and development efforts across our company’s biologics and vaccines portfolio.

Our DRE organization is responsible for the invention and application of new data‑rich tools that support scientists across process research and development at our company. We aspire to embed data‑intense technologies into the fabric of our company’s process development culture. This senior scientist position solves complex process research and development challenges in an interdisciplinary, collaborative environment via invention, development, and application of cutting‑edge process modeling, including novel deep neural network architectures and especially their combination with physics and theory through hybrid methods.

Ultimately, through the development, application, and deployment of these capabilities across our multimodality pipeline, we aim to elucidate a deeper understanding and optimization of our processes and drive enhanced decision‑making for improving the speed and quality of development for diverse medicines and vaccines.

Combine process modeling and deep learning techniques to help advance process‑focused scientific research. This includes, but is not limited to, developing mechanistic, hybrid, or data‑driven models for chemical synthesis and isolation, cell culture, fermentation, biomolecule separations; working closely alongside pipeline project teams to extract impactful insights from experimental data for manufacturing process design; employing process models to accelerate and enhance laboratory development through ML‑assisted optimal experiment design;

applying physics‑based and hybrid models to guide and de‑risk scale‑up / scale‑down during technology transfer and commercialization; developing new hybrid machine learning frameworks based on customized deep learning architectures and their integration with physics‑based models or structures; and collaborating with enterprise IT colleagues for the automated scaling, training, and serving of such models to a wide process development organization. The candidate should demonstrate a robust background in machine learning with an emphasis on the intersection of modeling and manufacturing process development.

• A Ph.D. in Engineering, Biology, Chemistry or a closely‑related field
• A M.S. in Engineering, Biology, Chemistry or a closely‑related field with at least 2 years of industrial/pharmaceutical experience
• A B.S. in Engineering, Biology, Chemistry or a closely‑related field with at least 4 years of industrial/pharmaceutical experience

• Background and experience in process modeling, scientific machine learning, deep learning, and technology deployment
• Expertise in Python with hands‑on experience in deep learning frameworks and libraries (PyTorch, JAX, Tensor Flow, or similar)
• High motivation and enthusiasm for novel technology development; passion for modernizing chemical and bioprocess development practices
• Excellent communication skills, demonstrated creativity, adaptive problem‑solving, and effective interpersonal skills for delivering complex technical solutions in a dynamic cross‑functional environment
• Demonstrated scientific expertise evidenced through impactful journal publications and/or conference presentations

• Experience in developing novel process models for dynamical systems, process control, or digital twins for chemical or biological systems through physics‑based, mechanistic, or hybrid model frameworks
• Background in laboratory chemical or biochemical…