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 QualificationsEducation 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 The annual base pay (or hourly rate of compensation) for this position ranges from $101,643.20 - $152,464.80. Our positions offer eligibility for various incentives—an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles. Benefits offered include qualified retirement programs, paid time off (i.e., vacation, holiday, and leaves), as well as health, dental, and vision coverage in accordance with the terms of the applicable plans. Date Posted 05-Nov-2026 Closing Date 29-Nov-2026 Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In furtherance of that mission, we welcome and consider applications from all qualified candidates, regardless of their protected characteristics. If you have a disability or special need that requires accommodation, please complete the corresponding section in the application form. AstraZeneca is a global, science-led, patient-focused biopharmaceutical company. We focus on discovering, developing and commercialising prescription medicines for some of the world’s most serious diseases. But we are more than one of the world’s leading pharmaceutical companies. At AstraZeneca, we’re dedicated to being a Great Place to Work. Where you are empowered to push the boundaries of science, challenge convention and unleash your entrepreneurial spirit. To embrace differences and take bold actions to drive the change needed to meet global healthcare and sustainability challenges. There is no better place to make a difference in medicine, patients, and society. An inclusive culture where you will connect different thinking to generate new and valuable opportunities. Where you will find a commitment to lifelong learning, growth and development for all. Our Inclusion & Diversity (I&D) mission is to create an inclusive and equitable environment where people belong, using the power of our diversity to push the boundaries of science to deliver life-changing medicines to patients. Inclusion and diversity are fundamental to the success of our company, because innovation requires breakthrough ideas that only come from a diverse workforce empowered to challenge conventional thinking. We’re curious about science and the advancement of knowledge. We find creative ways to approach new challenges. We’re driven to make the right choices and be accountable for our actions. As an organisation centred around what makes us human, we put a big focus on people. Across our business, we want colleagues to wake up excited about their day at the office, in the field, or in the lab. Along with our purpose to bring life-changing medicines to people across the globe, we have a promise to you: to help you realise the full breadth of your potential. Here, you’ll do work that has the potential to change your life and improve countless others. And, together with your team, you’ll shape a culture that unites and inspires us every day. This is your life at AstraZeneca.

Senior Engineer, Process Data Science and Statistics.

more at Astrazeneca.

Head of Diagnostics - ANZ

Senior Director- Global Development Scientist

Manager, Market Access Policy Implementation

similar roles.

Lead Data Engineer

Senior Data Engineer

Inventory Control Analyst (Hybrid)

Data Scientist - EMEA

Physics AI Scientist III