Principal Scientist, Process Modeling, Digital Insights

Summary

We are seeking a Principal Scientist to join our Digital Insights team within the Development Sciences and Clinical Supply Digital Technologies organization (DDT). Digital is the multiplier that will allow DSCS to deliver better experiments faster, efficient filing and launch, more robust supply chains and higher-confidence decisions across the portfolio. The DSCS Digital Technologies organization is responsible for the invention and application of new digital tools/workflows to support scientists across drug substance development, drug product development and analytical development.

We aspire to embed digital technologies into the fabric of DSCS culture to drive transformational impact across the CMC space. In this Principal Scientist role, the successful candidate will apply first-principles engineering, computational fluid dynamics (CFD), and physics-based simulation to bring predictive rigor to scale-up across a multi-modality pipeline from small to large molecules, with a current focus on biologics.

This role will serve as our senior technical authority on CFD and engineering simulation for drug substance development, with an emphasis on bioreactor scale-up/scale-down and other sensitive bioprocess unit operations. They will lead physics-based assessments of mixing (P/V, shear, pH gradients) and gassing (kLa, O2 distribution) to define engineering equivalence between scales and to inform scale-down model design.

• Lead CFD and engineering simulation of bioreactor mixing and gassing to characterize P/V, shear, mixing time, pH and component gradients, kLa, and gas gradients toward defining engineering equivalence between development and manufacturing scales.
• Design scale-down models that reproduce the critical hydrodynamic and mass-transfer environment of the manufacturing-scale bioreactor, in partnership with process development scientists.
• Extend physics-based modeling to other sensitive bioprocess unit operations, including harvest (centrifugation, depth filtration) and TFF (UF/DF, viral filtration), to quantify shear and hydrodynamic risk to product quality.
• Own end-to-end modeling project execution: problem framing, geometry and mesh strategy, solver setup, validation against experimental data, and clear communication of predictions and their limitations to cross-functional stakeholders.
• Support vessel and site characterization studies and standard workplans to support enterprise scale-up/scale-down strategy.
• Mentor junior scientists on the Process Modeling & Analytics team; grow their technical judgment in transport phenomena, CFD methodology, and simulation-based decision making.
• Shape the team's modeling roadmap and establish practical standards for simulation workflows (case setup, HPC/cloud use, validation, reuse) that scale across the portfolio.
• Support engineering simulations extending from biologics to small-molecule organic drug substance unit operations (e.g., crystallization, reactor mixing) as pipeline needs require.

• Ph.D. in Chemical Engineering, Mechanical Engineering, Bioengineering, Physics, Biology, Chemistry, Data Science or a closely-related field with at least 6 years of industrial/pharmaceutical or relevant experience.
• M.S. in Chemical Engineering, Mechanical Engineering, Bioengineering, Physics, Biology, Chemistry, Data Science or a closely-related field with at least 8 years of industrial/pharmaceutical or relevant experience.
• B.S. in Chemical Engineering, Mechanical Engineering, Bioengineering, Physics, Biology, Chemistry, Data Science or a closely-related field with at least 10 years of industrial/pharmaceutical or relevant experience.

• Chemical engineering training (or closely related) with deep grounding in transport phenomena, fluid mechanics, mixing theory, and gas-liquid mass transfer.
• Extensive hands-on CFD expertise using tools such as M-Star CFD, ANSYS Fluent, STAR-CCM+, COMSOL, or OpenFOAM-including geometry, meshing, solver setup, and post-processing for stirred-tank and gas-liquid systems.
• Demonstrated experience characterizing bioreactors at development and manufacturing scale: quantifying power input, tip speed, shear, mixing times, kLa, and gas hold-up, and connecting those outputs to scale-up decisions.
• Track record of designing scale-down models and defining engineering-equivalence criteria that hold up when a process moves between scales.
• Ability to validate simulations against experimental data and to…