The Data Enablement and Analytics (DEA) team within the PAPD (Product, Analytics and Process Development) organization drives PAPD’s digital transformation by making data usable, useful, and impactful in support of our mission of Transforming Therapeutic Molecules into Products for a Diversified Pipeline.

We are seeking a Process Development Engineer III, AI and Data Science to join our Artificial Intelligence (AI) and Advanced Analytics (AA) team in DEA, who pairs deep bioprocess‐engineering expertise with sophisticated AI/ML and Data Science (DS) capabilities to accelerate biologics development and manufacturing.

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 Process Development Engineer III 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. These include digital twins, advanced predictive modelling, and process control techniques.
• 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.

This Role Might Be For You If You Have:

• 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

Required Qualifications

• This role requires a Ph.D. in Chemical/Biochemical Engineering, Biotechnology, Applied Mathematics, Computer Science or related field with 0-2+ years of industrial experience OR- Master’s with 7+ years.
• Expert programming proficiency in Python and experience with statistical/computational tools such as JMP, SIMCA or MATLAB is required.
Proven ability to communicate technical concepts to multidisciplinary stakeholders.

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.
• Ability to deal with data from both SQL and NoSQL systems to support analytics, real-time processing, and application performance is a plus.
• Publication record in bioprocess modeling or AI for biomanufacturing is a plus.
• Mechanistic understanding of upstream and/or downstream bioprocess unit operations, scale-up/down principles, and critical quality attributes is strongly preferred.
• 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.
• Some experience with GenAI stacks (LLMs, vector databases, RAG pipelines) and multimodal techniques is necessary/required/strongly preferred.