Director, Process Data Science and Statistics lead

We are seeking an experienced leader, Director, Process Data Science and Statistics lead, Process Engineering, within the Cell Therapy Technical Operations function. This role is responsible for end-to-end statistical strategy, data infrastructure, and AI/ML capabilities that enable regulatory submissions, process characterization, and commercial manufacturing readiness for pivotal-stage cell therapy programs.

The Director will own statistical and digital strategies for comparability assessments, process characterization studies, and commercial process monitoring of the cell therapy programs. This role builds and maintains data systems, analytical platforms, and AI-enabled tooling that connect process characterization and manufacturing execution data to statistical analysis and decision support - scaling from early process development through late-stage clinical development and commercial and post-commercial manufacturing.

This position reports to the Executive Director, Process Engineering, Cell Therapy Development and Operations, and is located in either Santa Monica, CA or Gaithersburg, MD.

Accountabilities

Statistical Strategy & Regulatory Support

• Own end-to-end statistical strategy for comparability assessments (site transfers, significant process changes), process characterization, and aids in control strategy development for pivotal-stage cell therapy programs

• Apply rigorous statistical frameworks (equivalence testing, process capability analysis, variance component analysis, tolerance intervals) to evaluate drug product quality and generate defensible regulatory narratives under tight timelines

• Support authoring CMC sections of IND amendments and regulatory submissions; respond to agency questions on manufacturing data and statistical methods

• Design and analyze DOE studies for process characterization and LVV manufacturing, including risk-based parameter selection and interaction modeling

• Support process and product characterization for early development activities

Process Monitoring & Reporting

• Design and maintain process monitoring programs for pivotal and commercial manufacturing, including statistical control charts, alert/action limits, and trend detection

• Support CMC process and product characterization analysis during early development

• Build structured interactive reports and dashboards delivering real-time batch visibility, in-process trend tracking, and decision support across programs

• Support efforts to incorporate AI and ML technologies during early CMC process development

Data Infrastructure & Digital Systems

• Partner with IT to define requirements for data pipelines and analytical platforms (including tools such as Snowflake, Streamlit applications, other cloud platforms enabling automated reporting) that connect process development and manufacturing execution data to statistical analysis and decision support

• Drive manufacturing data digitization strategy, working with IT and manufacturing sites to establish scalable data capture and integration for commercial readiness

• Collaborate with IT to build and maintain data systems including interactive applications for process simulation, lab equipment data integration, and LLM-enabled data analysis and review

• Develop and deploy AI/ML solutions for process development and manufacturing process understanding, including predictive modeling, clustering, and automated data analysis workflows

• Support data digitization strategy for process optimization.

Technical Leadership & Mentorship

• Shape long-term digital manufacturing and data science strategy, driving AI/ML integration and scalable infrastructure to enable commercial readiness

• Support data science strategy and strategic plans for incorporating AI/ML during early CMC Process Development.

• Mentor scientists and engineers in statistical rigor, DOE principles, and applied data science to elevate organizational capability

• Collaborate cross-functionally with R&D, MSAT, Manufacturing Operations, Quality, and IT to align data capabilities with program needs

Required Qualifications

Education

• Ph.D. in Chemical Engineering, Biochemical Engineering, Biotechnology, Data Science, Statistics, or related field with 6+ years of industry experience

• OR M.S. in Chemical Engineering, Biochemical Engineering, Biotechnology, Data Science, Statistics, or related field with 10+ years of industry experience

• OR B.S. with 12+ years of hands-on industry experience

Technical Skills

• Demonstrated expertise in applied data science and statistical methods for process development or manufacturing (equivalence testing, process capability, variance components, DOE, control charting)

• Proficiency in Python (pandas, scipy, statsmodels, scikit-learn, plotly) and SQL for data analysis and pipeline development

• Experience with software development practices including version control, automation, and reproducible analysis workflows

• Experience supporting regulatory submissions (IND, BLA) with statistical analysis and CMC documentation

• Strong communication skills with ability to translate complex statistical findings into actionable insights for cross-functional audiences and regulatory-ready narratives

Soft Skills

• Excellent cross-functional communication and collaboration in matrixed environments

• Ability to work independently, set priorities, and deliver under time-sensitive regulatory timelines

• Strategic thinking with ability to shape long-term data and analytics roadmaps

Preferred Qualifications

• 5+ years of experience working with cloud data systems (AWS, Snowflake, Databricks, or similar)

• Experience building interactive analytical applications (Streamlit, Dash, or similar)

• Experience with AI/ML methods applied to manufacturing or bioprocess problems, including LLM-based tooling

• Experience in cell therapy or biologics manufacturing

• Experience with DOE design and analysis for process characterization

• Track record of building and deploying automated reporting and process monitoring systems