Senior Scientist, Deep Learning Engineer within Data-Rich Experimentation

Job Description

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 that we develop are as diverse as the team developing them, and in this Senior Scientist role, the chosen 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 to support the 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 role is a scientific position tasked with solving 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 multi-modality 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.

In this Senior Scientist role, the chosen candidate will 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