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, pairing bioprocess engineering expertise with AI/ML capabilities to accelerate biologics development and manufacturing. Design, implement, and operationalize models for upstream and/or downstream processes, turning data into prescriptive guidance and deploying production-grade models to enhance process understanding, optimization, and automation.

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 for 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 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, 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 (plus).
• Expert programming proficiency in Python and SQL; experience with JMP, SIMCA, MATLAB (helpful).
• Proven ability to communicate technical concepts to multidisciplinary stakeholders.

Skills

• Cloud analytics platforms (e.g., Dataiku, Databricks).
• Quality-by-Design (QbD) principles and Design-of-Experiments (DoE) for design space and robust control strategies.
• PAT and chemometric modeling (e.g., Raman spectroscopy) for bioprocess monitoring and control.
• Operations research techniques (e.g., combinatorial optimization, linear programming, MILP).
• GenAI stacks (LLMs, vector databases, retrieval-augmented generation) and multimodal techniques.
• Publication record in bioprocess modeling or AI for biomanufacturing (preferred).

Education

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