Astrazeneca

Overview

Define and drive the AI methodology agenda for one or more programmes within Clinical AI, leading by scientific influence and matrix coordination rather than through a formal hierarchy, developing reusable methods and enterprise-scale approaches that measurably advance the late-stage drug pipeline.

About Astrazeneca

At AstraZeneca, technology and science meet to change what is possible for patients. We are 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.

Requirements & Eligibility

• PhD in Computer Science, Machine Learning, Statistics, Mathematics, Biomedical Informatics, Computational Biology, or a closely related quantitative discipline
• 4–8 years of post-PhD experience in AI and machine learning method development with impact in clinical, biomedical, or drug development settings
• Deep experience, knowledge, and understanding of one or more fields of biology, with hands-on experience working with biological data
• Deep expertise in modern AI methodologies, including foundation model training and fine-tuning, Bayesian inference, temporal and longitudinal modelling, multimodal integration, model calibration and domain adaptation
• Exceptional software engineering skills: Python, deep learning frameworks (e.g. PyTorch), frontier coding agent frameworks, modern LLM tooling, and cloud platforms (e.g. AWS, Azure, GCP)
• Demonstrated experience translating AI methods into applications that inform clinical and/or biomedical decisions

Key Responsibilities

• Define and drive the AI methodology roadmap for assigned Clinical AI programmes, spanning early and late phase clinical development
• Lead, by matrix influence and scientific authority, the delivery of the most complex and high-stakes AI projects
• Develop and govern reusable, enterprise-grade AI methods and evaluation frameworks for clinical trial settings
• Champion data-centric AI practices at programme level: govern the acquisition, curation, and quality control of datasets
• Partner with Clinical Development, Biometrics, Regulatory, and Study Teams to embed AI strategy and validated solutions into study design
• Shape the AI evidence component for regulatory submission packages
• Evaluate and champion cutting-edge AI methodologies proposing fit-for-purpose approaches
• Establish and maintain external collaborations with academic institutions, technology partners, and industry consortia