Senior Engineer, Process Data Science and Statistics

The Sr Engineer will develop and maintain analytical workflows for multivariate process monitoring, predictive modeling of batch outcomes from incoming material, and anomaly detection on high-dimensional process data. 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. The role will also support development of hybrid mechanistic and data-driven models and human-supervised agentic AI applications.

This position reports to the Director, Process Data Science and Statistics lead, Process Engineering, Cell Therapy Development and Operations, and is located Required Qualifications

Education

Ph.D. in Chemical Engineering, Biochemical Engineering, Biotechnology, Data Science, Statistics, or related field with 2+ year of relevant experience

OR M.S. in Chemical Engineering, Biochemical Engineering, Biotechnology, Data Science, Statistics, or related field with 4+ years of relevant experience

OR B.S. in Chemical Engineering, Biochemical Engineering, Biotechnology, Data Science, Statistics, or related field with 5+ years of relevant experience

Technical Skills

Demonstrated experience in applied data science and statistical methods for process development or manufacturing (process capability, variance components, DOE, control charting, and multivariate analysis)

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

Experience developing and evaluating predictive models, including data preparation, validation, and interpretation of model results

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

Ability to prepare clear technical documentation and translate complex statistical findings into actionable insights for cross-functional audiences

Soft Skills

Excellent cross-functional communication and collaboration in matrixed environments

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

Practical problem-solving with ability to translate analytical concepts into maintainable tools and workflows

Preferred Qualifications

Experience working with cloud data systems (AWS, Snowflake, Databricks, or similar)

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

Experience with multivariate SPC methods, including PCA/PLS, Hotelling T-squared, SPE/DModX, and anomaly detection

Experience with hybrid mechanistic and data-driven modeling, process simulation, digital shadows, or neural ODEs

Experience with AI/ML methods applied to manufacturing or bioprocess problems, including incoming-material batch-outcome prediction and LLM-based or agentic 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

Accountabilities

Statistical Strategy & Regulatory Support

• •Support statistical analysis for comparability assessments (site transfers, significant process changes), process characterization, and control strategy development for cell therapy programs
• •Apply rigorous statistical frameworks (equivalence testing, process capability analysis, variance component analysis, tolerance intervals) to evaluate drug product quality and support defensible regulatory narratives under tight timelines
• •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

• •Develop and maintain process monitoring workflows for pivotal and commercial manufacturing, including statistical control charts, alert/action limits, and trend detection
• •Build multivariate statistical process control capabilities (e.g., principal component analysis or partial least squares), to support continued process verification, batch review, and investigations
• •Develop and evaluate anomaly detection methods for high-dimensional process data to support investigation triage and batch review
• •Build structured interactive reports and dashboards delivering batch visibility, in-process trend tracking, and decision support across programs
• •Develop predictive models linking incoming material attributes to batch outcomes to support assessment ahead of manufacturing run commitment

Data Infrastructure, Digital Systems and AI/ML

• •Partner with IT to implement data pipelines and analytical platforms (including tools such as Snowflake, Streamlit applications, and other cloud platforms enabling automated reporting) that connect process development and manufacturing execution data to statistical analysis and decision support
• •Support manufacturing data digitization, working with IT and manufacturing sites to establish scalable data capture and integration for commercial readiness
• •Develop and evaluate hybrid mechanistic and data-driven models, including digital shadows and, where appropriate, for process understanding, forecasting, and decision support
• •Build and maintain reproducible AI/ML workflows, including data preparation, model evaluation, documentation, and monitoring, with emphasis on explainability and traceability
• •Develop constrained, human-supervised agentic AI applications for knowledge retrieval, investigation and deviation triage, digital batch-record assistance, scheduling support, and guided decision support

Technical Leadership & Mentorship

• •Contribute to digital manufacturing and data science capability development, supporting AI/ML integration and scalable infrastructure to enable commercial readiness
• •Translate process and manufacturing questions into practical analytical solutions, prioritizing multivariate process monitoring and incoming-material predictive modeling while supporting evaluation of emerging methods
• •Share statistical methods, reusable code, and applied data science practices with scientists and engineers 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 2+ year of relevant experience
• •OR M.S. in Chemical Engineering, Biochemical Engineering, Biotechnology, Data Science, Statistics, or related field with 4+ years of relevant experience
• •OR B.S. in Chemical Engineering, Biochemical Engineering, Biotechnology, Data Science, Statistics, or related field with 5+ years of relevant experience

• •Technical Skills
• •Demonstrated experience in applied data science and statistical methods for process development or manufacturing (process capability, variance components, DOE, control charting, and multivariate analysis)
• •Proficiency in Python (pandas, scipy, statsmodels, scikit-learn, plotly) and SQL for data analysis and pipeline development
• •Experience developing and evaluating predictive models, including data preparation, validation, and interpretation of model results
• •Experience with software development practices including version control, automation, and reproducible analysis workflows
• •Ability to prepare clear technical documentation and translate complex statistical findings into actionable insights for cross-functional audiences

• •Soft Skills
• •Excellent cross-functional communication and collaboration in matrixed environments
• •Ability to work indepe