Director of AI Research, AI for Oncology Clinical Development

Stack

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 delivery of complex, high-stakes AI projects by matrix influence and scientific authority
• ▹Evaluate, develop and champion cutting-edge AI methods end-to-end: 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 study design, execution and decision-making
• ▹Establish and maintain external collaborations with academic institutions, technology partners and industry consortia
• ▹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 and help shape team culture
• ▹Mentor and support more junior scientists

Requirements

• ▹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 of the PhD with measurable impact (e.g. models delivered, patents, SaMD filings, first-author publications, open-source projects, standards-body participation)
• ▹Exceptional software development and coding skills, leveraging frontier coding agent frameworks
• ▹Deep experience and understanding of one or more fields of biology
• ▹Deep understanding of machine learning fundamentals, with domain expertise in one or more of: 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 training, post-training, benchmarking and evals), model and data evaluations and benchmarking, model interpretability, model post-training and alignment

Nice to have

• ▹Deep expertise in cancer biology
• ▹Experience with biological data such as molecular (DNA, RNA, protein), imaging (radiology, microscopy) or clinical text (EHR, clinical notes)
• ▹Experience in drug development, including clinical trial design, biomarker discovery, companion diagnostics, dosing, safety, endpoints and regulatory
• ▹Experience in a matrixed global organization spanning multiple sites and therapy areas
• ▹Knowledge of computing hardware

Soft skills

About the company

AstraZeneca's AI Science & Innovation group sits at the centre of the company's R&D AI transformation, building AI models and agents from discovery to clinical development.