Senior Data Scientist, CMC

Build and apply data models, analytics, and digital tools to support pharmaceutical process development and manufacturing, from drug substance and drug product through packaging and labeling. The work spans experimental and process data, technology transfer, and manufacturing decisions, in collaboration with scientific, engineering, quality, and external manufacturing teams. The role is hands‑on and may be performed remotely, with travel as needed.

• Develop, validate, and deploy mechanistic, hybrid, and data‑driven models for process development, technology transfer, and manufacturing.
• Analyze design‑of‑experiments, process analytical technology, in‑process control, batch record, and related data to inform process understanding, control strategies, and risk decisions.
• Create analytics for unit operations such as blending, granulation, compression, coating, and fill/finish, as well as packaging and labeling workflows.
• Build traceable, reusable data pipelines and products that combine internal and external partner data; create dashboards for process performance and transfer readiness.
• Set priorities for process modeling and transfer analytics, and apply AI and machine learning to data review and knowledge capture with suitable validation and data‑integrity safeguards.
• Present recommendations, contribute to regulatory filings and data science strategy, and mentor colleagues in modeling and analytics.

• A master’s degree in a relevant scientific or engineering field, such as chemical engineering, pharmaceutical sciences, or data science; a bachelor’s degree with equivalent experience may be considered.
• At least five years of relevant work in biopharma process development, process engineering, or data science, ideally involving drug substance or drug product development or technology transfer.
• Experience with process modeling, design of experiments, statistical analysis, and data‑informed decisions.
• Practical Python and SQL skills, plus experience with Databricks or a comparable data platform and visualization tools.
• Working knowledge of cGMP, ALCOA+, CMC regulatory expectations, and validated GxP systems.

• Experience with AI/ML applications, packaging and labeling, regulatory filings, CDMOs or CROs, external data flows, product ownership, Agile practices, or change management.
• Familiarity with small‑molecule and oligonucleotide programs.