AI Research Lead, AI for Oncology Clinical Development

About the role

AstraZeneca's AI for Clinical Development team is looking for an AI Research Lead to help leverage AI alongside computational, statistical and machine learning tools in oncology clinical development. The role is in-office in Barcelona with a minimum of three days per week on site, with no remote or travel flexibility. It is a technical lead position working across the enterprise on the company's most pressing clinical development questions, in collaboration with cross-functional teams covering hematology, cell therapy, antibody-drug conjugates, small molecules and biologics.

Key responsibilities

• Contribute to the AI strategy and roadmap for oncology early and late phase clinical development.
• Serve as a key technical lead in matrixed teams delivering complex, high-stakes AI projects.
• Evaluate and develop cutting-edge AI methods across problem definition, data, governance, algorithm development, validation and adoption.
• Partner with clinical development, biometrics, regulatory and study teams to develop and validate AI solutions in clinical study design, execution, strategy and decision-making.
• Build 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.
• Mentor and support peers within the team.

Required profile

• PhD in a quantitative discipline such as computer science, mathematics, physics, biophysics, bioinformatics, computational biology, computational neuroscience or biostatistics.
• 2-5 years of work experience outside the PhD with measurable impact (models delivered, patents, SaMD filings, first-author publications, open-source projects, standards-body participation).
• Preferred: deep knowledge of the ML literature and connections with the ML community, and experience with biological data (molecular, imaging or clinical text) and drug development.

Required skills

• Exceptional software development and coding skills, using frontier coding agent frameworks; knowledge of computing hardware is a plus.
• Deep understanding of machine learning fundamentals, with domain expertise in one or more of: foundation model training and tuning, Bayesian inference, temporal modeling, multimodal modeling, model calibration and domain adaptation, data-centric AI, model evaluation and interpretability, multi-agent systems.
• Preferred: experience in clinical trial design, biomarker discovery, companion diagnostics, dosing, safety, endpoints and regulatory.
• Preferred: experience in a matrixed global organization and strong written and verbal communication.