Principal Scientist, Predictive Sciences

GSK

. Build and deploy advanced mechanistic, hybrid, and data-driven process models across upstream cell culture, chromatography, filtration, and formulation .

Posted 10/13/2026full-timeKing of Prussia • Pennsylvania • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying advanced mechanistic and data-driven process models, with a strong focus on integrating AI systems into CMC workflows. Proficient in model validation, compliance with regulatory requirements, and applying machine learning techniques to bioprocesses.

Highest-signal resume keywords

PhD In Data Science, Computational Science, AI/ML, Chemical Engineering, Biochemical EngineeringExperience With Python And Scientific Computing StacksExperience With CMC Development PrinciplesExperience Building Predictive Models For BioprocessesExperience With Agentic AI Frameworks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Predictive ModelingData-Driven Process ModelsMechanistic ModelsChemometric ModelsMachine LearningModel ValidationMLOpsCI/CDStatistical AnalysisIn-Silico Experimentation

Soft Skills

CollaborationCommunicationPresentation SkillsProblem-SolvingLeadership

Tools & Technologies

PythonMATLABGPROMSSIMCANumPySciPyPyTorchJMPDigital TwinsAI Systems

Industry Keywords

CMC DevelopmentRegulatory ComplianceBiologics Regulatory FrameworkFAIR Data PrinciplesModel Lifecycle ManagementData IntegrityQuality AssuranceAnalytical CharacterizationProcess DevelopmentScale-Up

Tech Stack

Tools & technologies

NumpyPythonPyTorch

About the role

Key responsibilities & impact

Build and deploy advanced mechanistic, hybrid, and data-driven process models across upstream cell culture, chromatography, filtration, and formulation
Develop digital twins and PAT chemometric models for continuous perfusion and capture platforms
Drive model-informed process development and characterization using MBDoE, model-based risk assessments, in-silico experimentation, and model-based monitoring and control strategies
Convert mathematical models into working computational tools using gPROMS, SIMCA, JMP, Python, MATLAB, or custom code
Validate models to meet GSK internal compliance and regulatory requirements
Champion digitally redesigned CMC workflows and embed modeling into process development and tech transfers
Build and sustain shared modeling infrastructure, tools, versioning, monitoring, and reliable model lifecycle management
Apply data integrity, quality, safety, policy, procedure, and intellectual-property principles
Design and build agentic AI systems that parse historical CMC data, propose experiments, call models, and surface recommendations
Integrate large language models and AI agents into CMC workflows
Develop AI-driven risk assessment and criticality ranking tools
Partner with laboratory informatics, data engineering, and technology platform teams on data-linked digital solutions
Champion FAIR data principles and structured data capture
Present modeling results and AI-derived insights to CMC teams, senior leadership, regulatory teams, and cross-functional partners
Author or co-author technical reports, model qualification packages, and regulatory submission sections
Publish, attend conferences, collaborate academically, and leverage external expertise to advance capabilities

Requirements

What you’ll need

PhD (or Master's with 2 years of industry experience) in data science, computational science, AI/ML, chemical engineering, biochemical engineering, computational biology, or a closely related field
Experience building predictive models for bioprocesses, including mechanistic, hybrid, data-driven, and chemometric models
Experience with Python and scientific computing stacks such as NumPy, SciPy, PyTorch, or similar, or equivalent tools including MATLAB, gPROMS, and SIMCA
Experience with CMC development principles, including process development, analytical characterization, scale-up, and the biologics regulatory framework (ICH Q8/Q9/Q10/Q11)
Experience applying machine learning to small-to-medium scientific datasets, including understanding of overfitting, uncertainty quantification, and model interpretability
Experience taking models into production use, including deployment, versioning, monitoring, MLOps, testing, packaging, and CI/CD
Experience building or working with agentic AI frameworks in a scientific or industrial context

Benefits

Comp & perks

Comprehensive benefits program for US employees (details provided in GSK US Benefits Summary)
Equal Opportunity Employer