AI Researcher, Associate Director, AI for Clinical Development

About the role

AstraZeneca is building an AI capability for clinical development within its AI Solutions division to accelerate the design, conduct and analysis of clinical trials across its Oncology and BioPharmaceuticals pipeline. This is an in-office role in Barcelona with a minimum of three days per week on site; remote or travel flexibility is not available. The role leads hands-on research and technical delivery of advanced AI models and agentic systems for clinical-development decisions and workflows.

Key responsibilities

• Own the end-to-end scientific and technical delivery of one or more clinical AI workstreams, from problem definition and data assessment through prototyping, validation, translation and iteration.
• Adapt emerging AI methods to clinical-development problems, investigate failure modes and make evidence-based recommendations.
• Build and evaluate agentic AI systems for use cases such as retrieval, tool use, code execution, memory, multi-step workflows and human oversight.
• Write high-quality Python and build reproducible research workflows using modern ML frameworks, version control, testing and cloud or high-performance computing environments.
• Act as a senior scientific and technical partner influencing clinical strategy, evaluation approaches, platform capabilities and investment decisions.
• Publish through peer-reviewed papers, reusable tools, patents, collaborations and conferences, and share work with external partners, consortia, regulators and the research community.
• Build the team's research culture through scientific review, constructive challenge and mentorship, and represent AstraZeneca externally.

Required profile

• MSc or PhD in Computer Science, Machine Learning, Biostatistics, Biomedical Informatics, Computational Biology or a closely related quantitative field, or a BSc with substantial equivalent experience.
• Independent delivery of a meaningful AI or ML research project, model, evaluation or software capability from question to reproducible result.
• Record of strong research or technical contributions such as peer-reviewed work, validated models, reusable software, datasets, benchmarks, patents or deployed capabilities.
• Expertise in topics such as unsupervised learning, dimensionality reduction, causal inference, survival or longitudinal analysis, Bayesian modeling, multimodal learning, domain adaptation and model interpretability.
• Comfortable with AI agent harnesses and quick to pick up new approaches.
• Desirable: experience with multimodal biomedical data, LLMs or agentic systems, clinical data standards, GxP, software as a medical device, model-risk management, scientific consortia, open-source work and mentoring scientists or engineers.

Required skills

• Strong Python and scientific-computing skills.
• Experience with a modern ML framework such as PyTorch, JAX or TensorFlow.
• Ability to explain technical findings, trade-offs and limitations to clinical, statistical, regulatory, engineering and leadership audiences.