Director of AI Research, AI for Oncology Clinical Development

This role can be based at AstraZeneca hubs in Barcelona, Spain or Cambridge, USWe'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.  About AISI AI Science & Innovation (AISI) sits at the centre of AstraZeneca's R&D AI transformation. Our remit is to build, buy and deliver the AI models and agents that change pipeline outcomes, across discovery, translational science, biomarkers and clinical development. Drug discovery has benefitted enormously in the current AI era, yet comprises only a portion of the journey to bring new treatments to those in need. The final step – clinical drug development – is oft overlooked, despite requiring a significant proportion of time and investment. In the AI for Clinical Development team at AstraZeneca, we're reimagining the process of clinical development. Our vision is to bring safe, efficacious treatments to patients in a way that quantifiably improves our chance to do this faster, more cost-effectively, and with reduced patient burden.  In this role, you will be a senior technical and strategic lead to help us leverage the power of AI to the fullest, alongside our other computational, statistical, and machine learning tools. You will work across the enterprise to define and deliver on AstraZeneca’s most pressing clinical development questions. You will proactively collaborate in cross-functional teams spanning AstraZeneca’s key Oncology foci of hematology, cell therapy, antibody-drug conjugates, small molecules, and biologics. This is an unprecedented, high visibility opportunity to invent new ways to leverage data, models, and learnings across the spectrum of cancer biology and drug modalities – and importantly, you and the team will apply these new methods to measurably advance the late-stage drug pipeline and our group’s ambition. Responsibilities Define and drive the AI strategy and roadmap for Oncology early and late phase clinical development, and align AI/ML priorities with clinical and business objectives Lead, by matrix influence and scientific authority, delivery of complex, high-stakes AI projects Evaluate, develop, and champion cutting-edge AI methods, end-to-end, including problem definition, data considerations, governance, algorithm development, validation, and adoption Build cross-functional relationships with clinical development, biometrics, regulatory, and study teams to embed AI strategy and validated solutions into clinical study design, execution, strategy, and decision-making Establish and maintain external collaborations with academic institutions, technology partners, and industry consortia to access novel capabilities and advance the AI roadmap Represent AstraZeneca at scientific conferences, standards bodies, and peer-reviewed venues; contribute first
- or last-author publications in leading ML and clinical AI journals Establish best practices; help shape and promote team culture Mentor and support more junior level scientists within the team Required qualifications PhD in a quantitative discipline such as computer science, bioinformatics, computational biology, mathematics, physics, biophysics, computational neuroscience, biostatistics 4-8 years’ work experience outside of PhD with measurable impact (e.g. models delivered, patents, SaMD filings, first-author publications, open-source projects, standards-body participation) Technical requirements Exceptional software development and coding skills, leveraging frontier coding agent frameworks; knowledge of computing hardware a plus Deep experience, knowledge, and understanding of one or more fields of biology Deep understanding of machine learning fundamentals, with domain expertise in one or more of the following Training and tuning foundation models Bayesian inference Temporal modeling Multimodal integration and modeling Model calibration and domain adaptation Data-centric AI: acquiring, creating, and curating datasets for model training / post-training / benchmarking / evals Model and data evaluations and benchmarking Model interpretability Model post-training and alignment Preferred skills Deep expertise in cancer biology  Experience working with biological data such as molecular (e.g. DNA, RNA, protein), imaging (radiology, microscopy), or clinical text (e.g. EHR, clinical notes) Experience in drug development incl