Principal Scientist, Predictive Biologics & AI-Driven CMC

GSK plc; King of Prussia (PA); On-site; USD 180,000 - 240,000; Full time

Job summary

GSK plc in the United States is seeking a Principal Scientist in the Predictive Sciences group within the CMC Digital & Knowledge Management department to advance predictive models for biologics process development and build AI-enabled tools that accelerate decision-making.

You will lead mechanistic, hybrid, and data-driven models across upstream and downstream operations, deploy them to production, and collaborate with process development, analytical development, and manufacturing science teams

Qualifications

• PhD or Master’s with industry experience in data science, AI/ML, or bioprocess engineering.
• Experience building predictive models for bioprocesses (mechanistic, hybrid, or data-driven).
• Proficiency in Python and scientific stacks (NumPy, SciPy, PyTorch).
• Understanding of CMC development principles and regulatory frameworks.
• Experience deploying models to production with versioning and MLOps.

Responsibilities

• Develop predictive models for upstream/downstream bioprocesses and deploy them.
• Design agentic AI systems to query data and aid decision making.
• Collaborate with process development, analytics, and manufacturing teams.
• Ensure model lifecycle management and regulatory alignment.
• Share results with senior stakeholders and contribute to regulatory submissions as needed.

Skills

Predictive modeling

Python programming

Machine learning

Model deployment

Agentic AI

Bioprocess understanding

Education

PhD or Master’s +2 years

Data science / computational field

Tools

Python

MATLAB

gPROMS

SIMCA

Job description

GSK plc in the United States is seeking a Principal Scientist in the Predictive Sciences group within the CMC Digital & Knowledge Management department to advance predictive models for biologics process development and build AI-enabled tools that accelerate decision-making.

You will lead mechanistic, hybrid, and data-driven models across upstream and downstream operations, deploy them to production, and collaborate with process development, analytical development, and manufacturing science teams