Associate Principal Scientist, Data Engineer, Digital Insights, DSCS Digital Technologies Full-time NEW

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. 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 the teams developing them, and in this Associate Principal Scientist role, the successful candidate will design, build, and maintain data pipelines that capture, curate, and deliver experimental and process data from Sterile Product Development (SPD) teams into downstream SPD digital initiatives, including mechanistic and data-driven process modeling as well as Bayesian optimization approaches. The pipelines built here will directly enable colleagues within Digital Insights to deploy a variety of modeling approaches for de-risking and optimizing sterile drug substance (DS) and drug product (DP) manufacturing processes across biologics and vaccines.

The ideal candidate will have hands-on experience in sterile drug product development and will have transitioned into a data engineering or data science role. This domain depth enables the candidate to work comfortably alongside SPD experimentalists, understand the scientific context of the data being captured, and anticipate the data needs of downstream modelers. Preferred is familiarity with mechanistic and data-driven modeling approaches so that the candidate can effectively support modelers by delivering data in the right format, granularity, and context.

Responsibilities:

• Build strong partnerships with SPD experimentalists, process engineers, and analytical scientists to gather requirements for data solutions which will have direct pipeline impact.
• 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 that make SPD data interoperable and readily consumable by downstream modeling and optimization workflows.
• 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 at the source and reduce friction between experimentation and modeling.
• Demonstrate excellent interpersonal, communication, and collaboration skills.
• Embrace and model our core values including fostering a supportive culture where all can thrive.
• Collaborate effectively in a dynamic, integrated, and multidisciplinary team environment.
• Perform impactful scientific innovation in a team-oriented manner that builds trusted partnerships across vast stakeholder networks.
• Publish and present research, including maintaining an established track record of interaction with the broader academic community.

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:

• Proficient in Python and/or another programming language (e.g., Java, R). Comfortable working in development environments such as Posit/RStudio/Jupyter.
• Solid SQL skills with hands-on experience writing and optimizing queries against relational databases.
• Experience with ETL/ELT (Extract, Transform, Load) 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 closely related pharmaceutical development — with a demonstrated transition into a data engineering, data science, or computational role.
• Working knowledge of how mechanistic and data-driven models consume and depend on experimental data — sufficient to anticipate modeler needs and deliver appropriately structured datasets.
• Ability to deliver complex solutions under compressed timelines in a dynamic environment.

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 formulation optimization studies, Bayesian optimization, or related model-guided experimental design approaches.
• Familiarity with mechanistic and/or data-driven modeling approaches, not necessarily as a modeler, but understanding input/output data requirements and validation data needs.
• Experience with common process and analytical capabilities commonly utilized in pharmaceutical development.
• Working knowledge of multivariate data analysis, SPC, and PAT, with experience integrating experimental and manufacturing data into models.
• Familiarity with laboratory data systems (ELN, LIMS, historian/SCADA systems, PAT) and how to extract structured data from them.
• Experience with data visualization tools (Shiny, Streamlit, Spotfire, Dash, PowerBI, or Tableau).
• Connecting to AWS (S3, RedShift, Glue, Athena, SageMaker) as a data source for data visualization.
• Experience with data pipeline tools such as Dataiku or Databricks.
• Knowledge of regulatory expectations relevant to sterile products and model-informed development (e.g., ICH Q8-Q12, process validation, data integrity).
• Evidence of cross-functional collaborations spanning laboratory, manufacturing, modeling, and digital teams.

Required Skills:

Adaptability, Analytical Method Development, Assay Development, Biological Assay Development, Cell-Based Assays, Chromatographic Techniques, Cross-Functional Teamwork, External Collaboration, High Resolution Mass Spectrometry (HRMS), Liquid Chromatography-Mass Spectrometry (LC-MS), Mass Spectrometry Analysis, Optimism, Process Analytical Technology (PAT), Protein Analysis, Teamwork

Preferred Skills:

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