Associate Principal Scientist, Hybrid Modeler, Digital Insights, DSCS Digital Technologies

Job description

Job Description We are a global biopharmaceutical leader with a different portfolio of prescription medicines, oncology, vaccines and animal health products. We are driven by our purpose to develop and deliver innovative products that save and improve lives. With 69,000 employees operating in more than 140 countries, we offer state-of-the-art laboratories, plants and offices that are designed to inspire our employees as we learn, develop and grow in our careers. We are proud of our over 125 years of service to humanity and continue to be one of the world’s biggest investors in Research & Development. We are seeking an Associate Principal Scientist to join our Digital Insights team within the Development Sciences and Clinical Supply (DSCS) Digital Technologies organization. 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. The tools that we develop are as different as the teams developing them, and in this Associate Principal Scientist role, the successful candidate will advance the company’s digital-first process development strategy by deploying mechanistic, CFD-based, and data-driven modeling approaches to design, de-risk, and optimize sterile drug substance (DS) and drug product (DP) manufacturing processes. The position will sit at the intersection of first-principles physics, advanced CFD, and machine learning / data science, enabling predictive understanding, robust scale-up, and accelerated clinical-to-commercial delivery across biologics and vaccines. The successful candidate will play a technical leadership role in building and applying mixing and unit-operation virtual twins, integrating CFD with experimental data and AI/ML methods, and translating model outputs into actionable CMC and manufacturing decisions. Responsibilities: Develop and deploy a portfolio of mechanistic, CFD, and data-driven models to support development, scale-up, tech transfer, and manufacturing of sterile DS and DP processes across a different biologics and vaccine pipeline. Lead CFD-based mixing and unit operation modeling (e.g., compounding, dilution, pumping, filling, filtration) to quantify hydrodynamic stresses, energy dissipation rates, mixing times, and scale-up risk—enabling science-based operating windows and control strategies. Integrate data science and machine learning with physics-based models to accelerate model execution, improve predictive accuracy, and enable rapid scenario screening. Collaborate closely with Sterile Product Development (SPD), Drug Substance, Manufacturing Science and Technology (MS&T), and Manufacturing teams to de-risk sterile process scale-up, optimize formulation and process robustness, and support clinical‑to‑commercial transitions across both small and large molecule modalities. Design, execute, and interpret scale‑down and validation experiments to establish model credibility and scalability. Use experimental data to validate and refine CFD and ML models. Provide technical leadership during critical investigations, including deviations, root-cause analyses, and process troubleshooting, using model-based insights to rapidly resolve product and process challenges. Own end-to-end modeling project execution, including problem formulation, data requirements, simulation workflows, model validation, reporting, and clear communication of predictions and uncertainty to cross‑functional stakeholders. Establish best practices for modeling workflows, including pre/post‑processing, HPC and cloud computing utilization, data management, version control, and model reuse. Contribute to standardized playbooks and a central model repository. Demonstrate excellent interpersonal, communication, and collaboration skills. Embrace and model our core values of inclusion, including fostering a supportive culture where all can thrive. Be able to effectively collaborate in a dynamic, integrated, and multidisciplinary team environment. Demonstrate a clear ability to perform impactful scientific innovation in a team-oriented manner that builds trusted partnerships across vast stakeholder networks. Contribute to the broader scientific community and enhance the company's reputation through a strong record of peer-reviewed publications and impactful conference presentations. Education Minimum Requirement: Ph.D. in Computer Science, Data Science, Engineering, Chemistry, Physics, Biology, Pharmaceutical Sciences, or a closely-related field with at least 3 years of industrial/pharmaceutical or relevant experience. M.S. in Computer Science, Data Science, Engineering, Chemistry, Physics, Biology, Pharmaceutical Sciences, or a closely-related field with at least 5 years of industrial/pharmaceutical or relevant experience. B.S. in in Computer Science, Data Science, Engineering, Chemistry, Physics, Biology, Pharmaceutical Sciences, or a closely-related field with at least 7 years of industrial/pharmaceutical or relevant experience. Required Experience and Skills: Strong expertise in CFD and transport phenomena, with hands-on experience using tools such as ANSYS Fluent, STAR‑CCM+, M‑Star, COMSOL, OpenFOAM, or equivalent. Demonstrated experience with multiphase and complex flows, including free‑surface modeling (VOF), turbulent flows, non‑Newtonian rheology, and/or particle‑laden systems. Strong programming and data science skills in Python, MATLAB, R, JMP, or equivalent, including data wrangling, visualization, model coupling, and workflow automation. Experience validating models against experimental data and designing representative scale‑down systems. Ability to translate complex modeling results into clear, actionable insights for non‑modeling audiences; strong written and verbal communication skills. Preferred Experience and Skills: Experience with sterile CMC development workflows, particularly unit operations such as mixing, pooling, pumping, filling, filtration, or freeze‑drying. Familiarity with regulatory expectations and trends supporting mechanistic modeling, digital twins, and model‑informed development in cGMP environments (e.g., ICH Q8–Q12, CFR, USP). Applied understanding of QbD, DOE, and model validation frameworks, including statistical design and analysis of experiments. Working knowledge of multivariate data analysis, SPC, and PAT, with experience integrating experimental and manufacturing data into models. Experience with advanced modeling approaches, such as:Reduced‑order modeling (ROM) Physics‑informed neural networks (PINNs) Hybrid mechanistic / machine learning models CFD‑ML surrogate models for rapid decision making Prior contributions to technology transfer, process robustness assessments, or troubleshooting using modeling and simulation are a strong plus. Required Skills: Adaptability, Adaptability, Analytical Method Development, Artificial Neural Networks (ANNS), Assay Development, Biological Assay Development, Cell-Based Assays, Chromatographic Techniques, Cross-Functional Teamwork, Data Merging, Data Modeling Techniques, Design of Experiments (DOE), External Collaboration, High Performance Computing (HPC), High Resolution Mass Spectrometry (HRMS), Liquid Chromatography-Mass Spectrometry (LC-MS), Machine Learning (ML), Mass Spectrometry Analysis, Mathematics Modeling, MATLAB, Model Building, Model Development, Neural Networks, Optimism, Perform Testing {+ 10 more} Preferred Skills: Current Employees apply HERE Current Contingent Workers apply HERE US and Puerto Rico Residents Only: Our company is committed to inclusion, ensuring that candidates c