Are you ready to harness advanced AI to reinvent how medicines are developed, released, and delivered to patients? Could you turn complex, cross-functional challenges into reusable capabilities adopted across development and supply sites worldwide? This is your opportunity to lead enterprise-scale AI that accelerates decisions and elevates productivity across Late Stage Development CMC and Supply Chain.
You will spearhead the delivery of Machine Learning, Foundation Models, and Agentic AI that transform how our teams develop, make, test, and supply medicines to patients. Your success will be measured by adoption, impact, and scale—connecting data foundations, AI technology, and process reinvention to create durable advantages for our end-to-end value chain.
Accountabilities:
People Leadership: Lead, coach, and develop a high-performing team of Data Scientists, Product Managers and Agentic AI specialists. Establish clear expectations, promote technical excellence, support continuous learning, and create an inclusive environment focused on delivery and impact.
Technical Delivery: Lead the application of machine learning, multi-agentic LLM, deep learning, and foundation model techniques to solve practical problems across Late Stage Development, CMC, and Supply Chain.
Model Development and Adaptation: Lead the development, adaption, fine-tuning, and training of ML, LLM and foundation models using appropriate data, modelling approaches, and performance objectives. Steer you team to select methods that are proportionate to the business need and suitable for deployment in a regulated environment.
Model Evaluation and Validation: Design robust evaluation frameworks to assess model performance, reliability, fairness, explainability, and usability. Use appropriate statistical methods and business-focused measures to demonstrate that solutions are fit for purpose and deliver sustained business impact.
Practical Solution Delivery: Lead delivery from problem definition and data exploration through prototyping, validation, deployment, and continuous improvement. Ensure solutions are technically robust, secure, maintainable, and capable of delivering measurable outcomes.
AI Product Management: Establish, in partnership with Operations IT an AI Product Management and MLOps operating model that enables rapid iteration, robust deployment, and reliable lifecycle management in the cloud.
Customer-Focused Problem Solving: Work closely with customers and subject-matter experts to understand their needs, translate business questions into analytical problems, and deliver solutions that improve decision-making, productivity, quality, or cycle times.
Cross-Functional Collaboration: Partner effectively with colleagues across CMC, Supply Chain, Operations IT and other relevant functions to integrate models into business processes and technology environments.
Process Reinvention: Partner with business functions across late-stage development, clinical and commercial supply to reimagine ways of working and embed AI into daily operations to drive productivity.
Standards and Governance: Define best practices, validation standards, and compliant deployment processes for ML, LLM, and Agentic AI; ensure strong data governance and model integrity.
Reuse and Knowledge Sharing: Establish strong cross company networks to encourage the reuse of proven methods, platforms, data products, and modelling approaches, while sharing technical insights and learnings across relevant teams and projects.
Outcome Measurement: Track adoption, model performance, user experience, and business impact, using evidence to prioritise improvements and focus team activity on measurable customer and operational outcomes.
Ecosystem Partnerships: Build external collaborations with academia and technology providers; maintain a deep view of emerging methods and tooling, selectively bring in capabilities that accelerate our roadmap, de-risk innovation and shape our strategic AI roadmap.
