This role can be based at our hubs in Gothenburg, Sweden; Macclesfield, UK; or Barcelona, Spain.

We're building a connected, end-to-end Enterprise AI engine - uniting data foundations, AI technology, process reinvention, and business-facing AI to accelerate results across the whole value chain. Success depends on being exceptional connectors: you'll actively leverage existing capabilities, celebrate and promote reuse, export breakthrough ideas across geographies and functions, and obsess over scaling impact rather than building in isolation. If you thrive in high-collaboration environments where your role is to turn complex, cross-functional problems into reusable, enterprise-wide capabilities - and where the measure of success is adoption and scale, not just innovation - you'll have the platform (and sponsorship) to make it real.  

What you’ll do

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.

Essential Skills/Experience:

• Minimum master’s in Data science, Computer Science, Statistics, Computational Chemistry/Biology, Engineering, or related field.
• Extensive work experience in data science (10+ years), with considerable time spent in a leadership role, preferably within the pharmaceutical, biotech, or healthcare industry.
• Proven experience in machine learning, deep learning, foundation model delivery and multiagent (LLM) systems, with a deep understanding of algorithms, architectures, and model optimization.
• Exposure to a range of industries and domains, with the ability to quickly understand and apply ML & Agentic AI solutions to diverse business problems.
• Experience of establishing and running AI Product Management organisations and partnering with IT to establish robust MLOps / AIOps ways of working.
• Strong programming skills with proficiency in ML, Foundation Model and Agentic development and deployment frameworks.
• Excellent leadership and team management skills, with the ability to inspire and guide the team.
• Exceptional communication and stakeholder management skills, with a demonstrated ability to translate complex technical concepts into clear business implications.
• Experience in deploying AI and ML approaches into Pharmaceutical CMC and Supply Chain Management organisations.

Desirable Skills/Experience:

• PhD or equivalent experience in Data Science, Computer Science, Statistics, Computational Chemistry/Biology, Engineering, or related field.
• Proven track record of publishing Foundation Model, LLM or Agentic AI results and tools in peer-reviewed journals, conferences, and other scientific proceedings.
• Experience working in regulated industries, with a strong understanding of data privacy, security, and compliance requirements (e.g., GDPR, EU AI legislation).

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.

Why AstraZeneca:

Here you will shape the future of digital healthcare with the backing and investment to win—pairing cutting-edge AI and cloud technology with a mission that directly impacts patients. We bring diverse expertise together to solve problems that span the enterprise, from development labs to global supply networks, turning innovative ideas into scaled capabilities that change how we work. You will be trusted to experiment, move fast, and build ecosystems inside and outside the company, while growing through hands-on learning, peer collaboration, and real-world delivery. We value ambition and kindness in equal measure, and we believe that bold thinking thrives when unexpected teams come together to unlock new possibilities for patients.

Call to Action:

Lead enterprise AI that transforms how life-changing medicines reach patients—step forward today to create impact at scale.

Date Posted

Closing Date