Director of AI Research, AI for Oncology Clinical Development

About the role

AstraZeneca is looking for a Director of AI Research to lead its AI for Oncology Clinical Development, a senior technical and strategic role within AI Science & Innovation (AISI). The position can be based at the Barcelona hub in Spain or the Cambridge hub in the US. You will define and deliver on the company's most pressing clinical development questions, working across oncology foci such as hematology, cell therapy, antibody-drug conjugates, small molecules and biologics, and applying AI methods to advance the late-stage drug pipeline.

Key responsibilities

• Define and drive the AI strategy and roadmap for oncology early and late phase clinical development, aligning AI/ML priorities with clinical and business objectives
• Lead complex, high-stakes AI projects through matrix influence and scientific authority
• Evaluate, develop and champion AI methods end-to-end, including problem definition, data considerations, governance, algorithm development, validation and adoption
• Build relationships with clinical development, biometrics, regulatory and study teams to embed AI strategy and validated solutions into clinical study design and decision-making
• Establish external collaborations with academic institutions, technology partners and industry consortia
• Represent AstraZeneca at conferences, standards bodies and peer-reviewed venues, and publish first- or last-author papers in leading ML and clinical AI journals
• Help establish best practices, shape team culture and mentor junior scientists

Required profile

• PhD in a quantitative discipline such as computer science, bioinformatics, computational biology, mathematics, physics, biophysics, computational neuroscience or biostatistics
• 4-8 years of work experience outside the PhD with measurable impact (models delivered, patents, SaMD filings, first-author publications, open-source projects, standards-body participation)
• Deep knowledge of one or more fields of biology

Required skills

• Exceptional software development and coding skills, using frontier coding agent frameworks; knowledge of computing hardware is a plus
• Machine learning fundamentals, with domain expertise in one or more of: foundation model training and tuning, Bayesian inference, temporal modeling, multimodal integration, model calibration and domain adaptation, data-centric AI, model evaluation and interpretability, model post-training and alignment
• Preferred: deep expertise in cancer biology, experience with molecular, imaging or clinical text data, drug development experience, and experience in a matrixed global organization