Senior Engineer, Process Data Science and Statistics

AstraZeneca•Gaithersburg, MD

About The Position

The Senior Engineer develops and maintains analytical workflows for multivariate process monitoring, predictive modeling of batch outcomes from incoming materials, and anomaly detection in high-dimensional process data. The role builds and maintains data systems, analytical platforms, and AI-enabled tools that connect process characterization and manufacturing execution data to statistical analysis and decision support, spanning early process development through late-stage clinical development and commercial and post-commercial manufacturing. It also supports hybrid mechanistic and data-driven models and human-supervised agentic AI applications. The position contributes statistical analysis and strategy for process characterization, comparability assessments, control strategy development, and manufacturing monitoring. It develops predictive and anomaly-detection methods, interactive reports, dashboards, data pipelines, and reproducible AI/ML workflows, while working with cross-functional teams to support manufacturing data digitization and commercial readiness. The role reports to the Director, Process Data Science and Statistics lead, Process Engineering, Cell Therapy Development and Operations. Location is not specified in the posting.

Requirements

Ph.D. in Chemical Engineering, Biochemical Engineering, Biotechnology, Data Science, Statistics, or a related field with at least 2 years of relevant experience; or M.S. in one of these fields with at least 4 years of relevant experience; or B.S. in one of these fields with at least 5 years of relevant experience.
Demonstrated experience applying data science and statistical methods to process development or manufacturing, including process capability, variance components, DOE, control charting, and multivariate analysis.
Proficiency in Python, including pandas, scipy, statsmodels, scikit-learn, and plotly, and in SQL for data analysis and pipeline development.
Experience developing and evaluating predictive models, including data preparation, validation, and interpretation of 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.
Excellent cross-functional communication and collaboration skills in matrixed environments.
Ability to work independently, set priorities, and deliver under time-sensitive program and regulatory timelines.
Practical problem-solving ability and skill in translating analytical concepts into maintainable tools and workflows.

Nice To Haves

Experience with cloud data systems such as AWS, Snowflake, Databricks, or similar.
Experience building interactive analytical applications such as Streamlit or Dash.
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 applying AI/ML methods 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.

Responsibilities

Support statistical analysis for comparability assessments, process characterization, and control strategy development for cell therapy programs.
Apply statistical frameworks—including equivalence testing, process capability analysis, variance component analysis, and tolerance intervals—to evaluate drug product quality and support regulatory narratives.
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.
Develop and maintain process-monitoring workflows for pivotal and commercial manufacturing, including control charts, alert/action limits, and trend detection.
Build multivariate statistical process control capabilities 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 interactive reports and dashboards for batch visibility, in-process trend tracking, and decision support.
Develop predictive models linking incoming-material attributes to batch outcomes to inform manufacturing run commitment.
Partner with IT to implement data pipelines and analytical platforms connecting process development and manufacturing execution data to statistical analysis and decision support.
Support manufacturing data digitization 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, for process understanding, forecasting, and decision support.
Build and maintain reproducible AI/ML workflows, including data preparation, model evaluation, documentation, and monitoring, emphasizing 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.
Contribute to digital manufacturing and data science capability development, including AI/ML integration and scalable infrastructure for 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.
Collaborate with R&D, MSAT, Manufacturing Operations, Quality, and IT to align data capabilities with program needs.