This role sits within Predictive Sciences, in the CMC Digital & Knowledge Management department, reporting to the Team Lead for LM Drug Substance Development modelling. You will work in close partnership with process development, analytical development, and manufacturing science teams, and collaborate with the broader GSK digital science and data communities to share platforms, capabilities, and best practices.
As a Principal Scientist you are an individual contributor with high scientific autonomy. You are expected to influence the direction of digital CMC through the quality of your technical contributions and the trust you build with scientific partners.
This role will provide you the opportunity to lead key activities to progress your career, these responsibilities include some of the following:
Modeling & Simulation
Build and deploy advanced process models: mechanistic, hybrid, and data-driven across upstream cell culture, chromatography, filtration, and formulation, including digital twins and PAT chemometric models for current and evolving continuous perfusion and capture platforms. Leverage engineering, biological, chemical, or materials science knowledge to postulate mechanisms for bioprocess behavior and develop or modify existing mathematical models to describe those mechanisms
Drive model-informed process development and characterization as an embedded matrix team member, using MBDoE and model-based risk assessments to define critical process parameters, design space, in-silico experimentation, reduce wet-lab cycle, accelerate process understanding, platform fit, and model-based monitoring and process control strategies.
Translate science into software by converting mathematical models into working computational tools via platforms (gPROMS, SIMCA, JMP) or custom code (Python, MATLAB), and ensure models are validated to meet GSK internal compliance and regulatory requirements.
Champion adoption of digitally redesigned CMC workflows across the Drug Development and Supply organization, supporting the CMC portfolio through GSK enterprise-wide digital transformation principles, enhancing digital fluency, and embedding modelling as a central pillar of process development and tech transfers. Given our high ambition for predict-first, every model is expected by default to transfer into use by the functions – in this case Large Molecule Drug Substance development – with a clear, credible path to routine use by function scientists – including documentation and reference to operating procedures – so that running the model does not depend on us.
Build and sustain the modeling ecosystem – not just individual models, but the shared infrastructure, tools and ways of working that keep them versioned, monitored and reliable over time. Align your work with this ecosystem so that what you build fits into it and strengthens it for others. Embed data integrity principles through model, data, and results, along with the requirements of quality, safety, and policies and procedures, and ensure that GSK intellectual property is protected.
Stay at the forefront of the field through publications, conferences, and academic collaborations, bringing new techniques into the organization to continuously raise the bar on what's possible. Leverage pre-competitive alliances and external expertise to advance capabilities.
Communicate and collaborate broadly – present results to CMC teams and senior leadership, and foster strong working relationships across R&D, pilot plants, and the commercial supply chain.
Agentic AI based CMC Workflows
Design and build agentic AI systems that autonomously parse historical CMC data, propose experimental designs and strategies, call specific models, and surface actionable recommendations to lab scientists and decision makers.
Integrate large language models and AI agents into CMC workflows – from literature mining and predictive intelligence to process insights, accelerated progress, and single cycle development.
Develop AI-driven risk assessment and criticality ranking tools to support process characterization and control strategy definition.
Data & Platform
Work closely with laboratory informatics, data engineering, and tech platform teams to ensure data-linked digital solutions from electronic lab notebooks, process historians, and analytical instruments.
Champion FAIR data principles and structured data capture practices across CMC development teams.
Contribute to shared modelling infrastructure, reusable components, and validated computational workflows.
Scientific Partnership
Embed as a trusted scientific partner within CMC teams – translating biological and process complexity into computable problems and translating model outputs back into practical development decisions.
Present modelling results and AI-derived insights to senior stakeholders, regulatory teams, and cross-functional partners.
Author or co-author technical reports, model qualification packages, and regulatory submission sections as needed.
Basic Qualifications
We are looking for professionals with these required skills to achieve our goals:
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, such as mechanistic (e.g., kinetic cell culture models, chromatography mass transfer models), hybrid, or data-driven and chemometric models.
Experience in Python (experience with scientific computing stacks such as NumPy, SciPy, PyTorch, or similar) or equivalent, including and not limited to MATLAB, gPROMS, SIMCA etc.
Experience with CMC development principles – process development, analytical characterization, scale-up, and the regulatory framework governing biologics (ICH Q8/Q9/Q10/Q11).
Experience applying machine learning to small-to-medium scientific datasets, with clear understanding of overfitting, uncertainty quantification, and model interpretability.
Experience taking models into production use – deployment, versioning, monitoring, and MLOps practice (testing, packaging, CI/CD) – sufficient to hand a model to non-modelling end users with confidence.
Experience building or working with agentic AI frameworks in a scientific or industrial context.
Preferred Qualifications
The following characteristics are not essential to carrying out the day-to-day functions of the job, but they are nice to have.
Familiarity with PAT (Process Analytical Technology) and real-time process control.
Experience with Design of Experiments and multivariate data analysis (MVDA) in a biopharmaceutical setting.
Exposure to regulatory filings (IND, BLA, NDA) and the role of models in CMC submissions.
Knowledge of digital twin platforms or process simulation tools (e.g., gPROMS, MATLAB, Aspen).
Skills
Artificial Intelligence (AI), Computational Sciences, Computer Programming, Data Analysis, Data Management, Data Modeling, Data Science, Predictive Modeling, Statistical Models
Please visit GSK US Benefits Summary to learn more about the comprehensive benefits program GSK offers US employees.
Why GSK?
Uniting science, technology and talent to get ahead of disease together.
GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together. We aim to positively impact the health of 2.5 billion people by the end of the decade, as a successful, growing company where people can thrive. We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines. We focus on four therapeutic areas: respiratory, immunology and inflammation; oncology; HIV; and infectious diseases – to impact health at scale.
People and patients around the world count on the medicines and vaccines we make, so we’re committed to creating an environment where our people can thrive and focus on what matters most. Our culture of being ambitious for patients, accountable for impact and doing the right thing is the foundation for how, together, we deliver for patients, shareholders and our people.
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