Associate Principal Scientist, Data Engineer, Digital Insights, DSCS Digital Technologies

Associate Principal Scientist, Data Engineer, Digital Insights, DSCS Digital Technologies

We are a global biopharmaceutical leader with a portfolio of prescription medicines, oncology, vaccines and animal health products. With 69,000 employees in more than 140 countries, we are committed to developing innovative products that save and improve lives.

We are seeking an Associate Principal Scientist to join our Digital Insights team within the Development Sciences and Clinical Supply (DSCS) Digital Technologies organization. In this role you will design, build, and maintain data pipelines that capture, curate, and deliver experimental and process data from Sterile Product Development (SPD) teams into downstream digital initiatives. Your work will enable modeling approaches for de‑risking and optimizing sterile drug substance (DS) and drug product (DP) manufacturing processes across biologics and vaccines.

• Build strong partnerships with SPD experimentalists, process engineers, and analytical scientists to gather requirements for data solutions.
• Design and implement robust, scalable data pipelines that ingest experimental and process data from SPD teams.
• Deliver analysis‑ready datasets to support SPD digital initiatives, including process modeling and Bayesian optimization.
• Define and enforce data standards, metadata schemas, and ontologies to make SPD data interoperable.
• Automate data ingestion from laboratory instruments, electronic lab notebooks, PAT systems, and manufacturing systems and integrate with cloud‑based storage and compute environments.
• Apply and generate data analysis and visualization workflows.
• Design and develop dashboards, reports, and data exports.
• Curate data and support definition of needs for automation of data ingestion.
• Influence digital data strategy for SPD by identifying opportunities to improve data capture practices and reduce friction between experimentation and modeling.
• Demonstrate excellent interpersonal, communication, and collaboration skills.
• Embrace and model our core values, fostering a supportive culture where all can thrive.
• Collaborate effectively in a dynamic, integrated, multidisciplinary team environment.
• Perform impactful scientific innovation that builds trusted partnerships across stakeholder networks.
• Publish and present research, maintaining a track record of interaction with the broader academic community.

• Ph.D. in Computer Science, Data Science, Engineering, Chemistry, Physics, Biology, Pharmaceutical Sciences, or related field with at least 3 years of industrial/pharmaceutical or relevant experience.
• M.S. in a related field with at least 5 years of industrial/pharmaceutical or relevant experience.
• B.S. in a related field with at least 7 years of industrial/pharmaceutical or relevant experience.

• Proficient in Python and/or another programming language (e.g., Java, R); comfortable with Posit/RStudio/Jupyter.
• Solid SQL skills with hands‑on experience writing and optimizing queries against relational databases.
• Experience with ETL/ELT processes and building data pipelines in a scientific or pharmaceutical context.
• Familiarity with cloud platforms (AWS, Azure, or GCP) for data storage, processing, and integration.
• Prior hands‑on experience in sterile drug product development, sterile DS and DP manufacturing processes, or related pharmaceutical development.
• Working knowledge of mechanistic and data‑driven models and their data requirements.
• Ability to deliver complex solutions under compressed timelines in a dynamic environment.

• Experience with sterile CMC development workflows, especially unit operations such as mixing, pooling, pumping, filling, filtration, or freeze‑drying.
• Familiarity with formulation optimization studies, Bayesian optimization, or related model‑guided experimental design approaches.
• Understanding of multivariate data analysis, SPC, and PAT, with experience integrating experimental and manufacturing data into models.
• Experience with laboratory data systems (ELN, LIMS, historian/SCADA, PAT) and extracting structured data.
• Experience with data…