Staff Engineer, Data Science (PMPD)

Role Summary

Staff Engineer, Data Science to join the Data Science team in the Data Enablement and Analytics (DEA) group within PMPD, blending bioprocess-engineering expertise with AI/ML to accelerate biologics development and manufacturing. Design, implement, and operationalize models for upstream and/or downstream operations, turning data into prescriptive guidance and deploying production-grade models.

Responsibilities

• Develop, validate, and maintain mechanistic, hybrid, and data-driven models for cell culture and/or purification processes.
• Translate complex bioprocess questions into quantitative modeling strategies informing scale-up, tech transfer, and continuous improvement.
• Advance PMPD‚Äôs broader data-science and digital-maturity initiatives.
• Collaborate with process engineers, citizen data scientists, IT, and manufacturing colleagues to coordinate modeling efforts enterprise-wide.
• Build and deploy AI/ML-powered digital solutions on cloud-based analytics platforms.
• Mentor citizen data scientists and champion best practices in model development, method selection, and code quality.
• Explore and prototype GenAI approaches to enhance knowledge management and decision support.

Qualifications

• Ph.D. in Chemical/Biochemical Engineering, Biotechnology, Applied Mathematics, or related field with 4+ years of industrial experience OR Master‚Äôs with 7+ years.
• Deep mechanistic understanding of upstream and/or downstream bioprocess unit operations, scale-up/down principles, and critical quality attributes.
• Demonstrated success modeling bioprocesses via first-principles, hybrid, or data-driven (ML) methods (preferred).
• Strong foundation in AI/ML algorithms (regression, classification, Bayesian methods, deep learning, time-series, probabilistic modeling) and multivariate statistics for process modeling, real-time monitoring, and control.
• Expert programming proficiency in Python and SQL; experience with JMP, SIMCA, MATLAB is helpful.
• Proven ability to communicate technical concepts to multidisciplinary stakeholders.

Skills

• Cloud analytics platforms (e.g., Dataiku, Databricks) ‚Äì preferred.
• Quality-by-Design (QbD) principles and Design-of-Experiments (DoE) for design space and robust control strategies ‚Äì preferred.
• PAT and chemometric modeling (e.g., Raman spectroscopy) for bioprocess monitoring and control ‚Äì preferred.
• Operations research techniques (e.g., linear programming, mixed integer programming) ‚Äì plus.
• GenAI stacks (LLMs, vector databases, RAG pipelines) and multimodal techniques ‚Äì plus.
• Strong publication record in bioprocess modeling or AI for biomanufacturing ‚Äì plus.

Education

• Ph.D. in Chemical/Biochemical Engineering, Biotechnology, Applied Mathematics, or related field with 4+ years of industry experience, or Master‚Äôs with 7+ years.

Additional Requirements

• Exposure to GenAI stacks and data-driven decision support is a plus.