Senior Process Data Scientist & AI Analytics

AstraZeneca plc; Gaithersburg (MD); On-site; USD 102,000 - 152,000; Full time

Job summary

AstraZeneca plc is seeking a Senior Engineer to develop and maintain analytical workflows for multivariate process monitoring and predictive modeling of batch outcomes. You will build AI-enabled tooling connecting process data to statistical analysis and decision support across development to manufacturing stages.

Responsibilities include creating scalable data systems, implementing hybrid mechanistic and data-driven models, and delivering explainable analytic tools for cross-functional teams.

Qualifications

• Experience in applied data science for process development or manufacturing.
• Ability to translate statistical findings for cross-functional audiences.
• Experience with DOE and multivariate analysis.

Responsibilities

• Develop and maintain process monitoring workflows for manufacturing.
• Build multivariate SPC capabilities to support process verification and batch review.
• Develop anomaly detection methods for high-dimensional process data.
• Create structured interactive reports and dashboards for batch visibility.
• Link incoming material attributes to batch outcomes to support forecasting.

Skills

Multivariate analysis

Python & SQL

Predictive modeling

Data pipelines

Documentation

Education

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

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

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

Tools

Snowflake

Databricks

Streamlit

Dash

Job description

AstraZeneca plc is seeking a Senior Engineer to develop and maintain analytical workflows for multivariate process monitoring and predictive modeling of batch outcomes. You will build AI-enabled tooling connecting process data to statistical analysis and decision support across development to manufacturing stages.

Responsibilities include creating scalable data systems, implementing hybrid mechanistic and data-driven models, and delivering explainable analytic tools for cross-functional teams.