Associate Director, AI for Oncology Clinical Development

In this role, you will be a technical 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

• Contribute to the AI strategy and roadmap for Oncology early and late phase clinical development
• Serve as a key technical lead and contributor in matrixed teams to deliver complex, high-stakes AI projects
• Evaluate and develop cutting-edge AI methods in one or more areas of problem definition, data considerations, governance, algorithm development, validation, and adoption
• Partner with clinical development, biometrics, regulatory, and study teams to develop and then validate novel AI 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
• Mentor and support peers within the team

Qualifications

• PhD in a quantitative discipline such as computer science, bioinformatics, computational biology, mathematics, physics, biophysics, computational neuroscience, biostatistics
• At least 2+ 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 including but not limited to clinical trial design, biomarker discovery, companion diagnostics, dosing, safety, endpoints, and regulatory
• Experience in a matrixed global organization spanning multiple sites and therapy areas
• Up to date with the latest AI research and tools, proactively trying out those of interest, and ability to discern hype from true added value