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Principal Scientist, Predictive Sciences

Posted 6 days ago

gskKing of Prussia (PA)

SENIORITY

Lead

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About the role

Principal Scientist, Predictive Sciences We are building the next generation of biologics CMC with a rapid, efficient, and predict-first mindset. As a Principal Scientist in the Predictive Sciences group within our new CMC Digital and Knowledge Management department, you will sit at the intersection of late-phase CMC process development and design of cutting-edge digital tools, predictive solutions, and artificial intelligence that accelerate how we bring medicines from bench to patient. Biologics CMC is undergoing a fundamental shift, moving away from empirical and experiment-heavy development towards a predict-first, digitally augmented, and model-informed development. You will help define what that shift looks like, building tools and ways of working that will have an impact on the entirety of portfolio and shape how we develop medicines. You will be an AI-native builder who takes mechanistic, hybrid and data-driven models from prototype to production. This role will deploy models through machine learning workflows and agentic AI systems that actively drive development decisions across upstream and downstream unit operations for biologics process development across our fed-batch, intensified fed-batch and continuous perfusion platforms. Strong modelling expertise is required and expected; equal weight is placed on the ability to ship those models into a form scientists can run independently, through deployment, product build and model lifecycle management. This role will also impact tech transfer, scale-up and commercialization of processes, and requires a proven ability to drive digital transformation principles across the business, with the agility to lead change. 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 Agentic AI based CMC Workflows Data & Platform Scientific Partnership
Basic Qualifications: 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:
  • 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 ModelsPlease 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. If you require an accommodation or other assistance to apply for a job at GSK, please contact the appropriate Recruitment Staff by emailing us at - usrecruitment.adjustments@gsk.comGSK is an Equal Opportunity Employer. This ensures that all qualified applicants will receive equal consideration for employment without regard to race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), parental status, national origin, age, disability, genetic information (including family medical history), military service or any basis prohibited under federal, state or local law.

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