Staff Engineer, AI and Data Science

You will design, implement, and operationalize AI and DS models for upstream (cell-culture/bioreactor), downstream (purification) operations, Formulation Development and multiple Analytics teams while partnering closely with process-development, manufacturing-sciences, and digital teams. You will turn data into prescriptive guidance, deploy production-grade models, and build innovative AI solutions that enhance process understanding, optimization, and automation.

A Typical Day in the Role of Staff Engineer Might Look Like:

• Build and deploy AI/ML-powered solutions to accelerate our digitalization journey.
• Advance PAPD's broader AI, DS and related digital-maturity initiatives.
• Collaborate with process engineers, citizen data scientists, IT, and manufacturing colleagues to coordinate AI and Advanced modeling efforts enterprise wide.
• Explore, prototype and implement GenAI approaches and solutions (e.g., Retrieval-Augmented Generation) to enhance knowledge management, and decision support.
• Develop, validate, and maintain mechanistic, hybrid, and data-driven models for cell culture, purification, formulation and other processes.
• Translate complex bioprocess questions into quantitative modeling strategies that inform scale-up, tech transfer, and continuous improvement.
• Mentor citizen data scientists and champion best practices in model development, method selection, and code quality.

• Analytical rigor and creative problem solving
• Ability to drive projects autonomously while thriving in cross-functional teams
• Excellent written and verbal communication
• Passion for innovation and continuous learning

This role requires a Ph.D. in Chemical/Biochemical Engineering, Biotechnology, Applied Mathematics, Computer Science or related field with 2+ years of industrial experience OR- Master's with 5+ years. Mechanistic understanding of upstream and/or downstream bioprocess unit operations, scale-up/down principles, and critical quality attributes is required. A demonstrated success modeling bioprocesses via first-principles, hybrid, or data-driven (ML) methods is preferred.

A strong foundation in AI/ML algorithms (regression, classification, Bayesian methods, deep learning, time-series, probabilistic modeling) is a plus, along with expertise in multivariate statistics for process modeling, real-time monitoring, and control. Expert programming proficiency in Python and SQL and experience with statistical/computational tools such as JMP, SIMCA, MATLAB is helpful. Proven ability to communicate technical concepts to multidisciplinary stakeholders a must. Experience with GenAI stacks (LLMs, vector databases, RAG pipelines) and multimodal techniques is necessary.

Preferred Qualifications

• Hands-on experience with cloud analytics platforms (e.g., Dataiku, Databricks).
• Strong working knowledge of Quality-by-Design (QbD) principles and statistically rigorous Design-of-Experiments (DoE) for defining design space, optimizing critical process parameters, and informing robust control strategies.
• Familiarity with PAT and chemometric modeling (e.g., Raman spectroscopy) for bioprocess monitoring and control.
• Understanding of operation research techniques such as combinatorial optimization, linear programming, mixed integer programming is a plus.
• Strong publication record in bioprocess modeling or AI for biomanufacturing.