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 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 #J-18808-Ljbffr GlaxoSmithKline