AI Researcher, Associate Director, AI for Clinical Development

This is an in-office role based in Barcelona, ES, with a requirement to work a minimum of three days per week on-site . Remote or travel flexibility is not available. Role Overview AstraZeneca is building a world-class AI capability for clinical development within AISI to accelerate the design, conduct, and analysis of clinical trials across our Oncology and BioPharmaceuticals pipeline. We are hiring a Senior Scientist to lead hands-on research and technical delivery of advanced AI models and agentic systems for clinical-development decisions and workflows. We are looking for scientists who can move easily between a clinical question and the AI system needed to investigate it. You might be developing a new method for analyzing randomized clinical trials, testing a multimodal model, or building an agent that can carry out a reproducible scientific workflow. The common thread is rigor: understanding what the evidence shows, what it does not show, and what would need to be true before a result could influence a trial. AI for clinical development is changing quickly. Foundation models, multimodal learning, agentic systems, post-training methods, and evaluation science are evolving alongside regulatory expectations. You do not need to have worked with every new framework, but you should be curious, learn quickly, and have strong judgment about when a new approach is genuinely useful. You will work at the intersection of frontier AI, clinical trial methodology, and biomedical science. You will work at the intersection of frontier AI, clinical trial methodology, and biomedical science. Your research may address treatment-effect heterogeneity, predictive biomarker discovery and validation, patient stratification, time-to-event outcomes, cross-trial generalization, and other problems that shape clinical-development decisions. Your work will also help establish how advanced AI systems are evaluated, governed, and used responsibly in the development of new medicines. Accountabilities 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 and apply emerging AI methods to clinical-development problems, investigate failure modes, and make clear recommendations based on the evidence Build and evaluate agentic AI systems for clinical-development use cases, including 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 across the organization, influencing clinical strategy, evaluation approaches, platform capabilities, and investment decisions. Share your work through peer-reviewed publications, reusable tools, patents, scientific collaborations, and conference presentations where appropriate. Build the research culture of the team through scientific review, constructive challenge, and mentorship of scientists and engineers. Represent AstraZeneca with external partners, scientific consortia, regulators, and the research community Essential Skills/Experience Hold an MSc or PhD in Computer Science, Machine Learning, Biostatistics, Biomedical Informatics, Computational Biology, or a closely related quantitative field, or have a BSc with substantial equivalent experience. Have independently delivered a meaningful AI or ML research project, workstream, model, evaluation, or software capability from initial question through a clear and reproducible result. Have a record of strong research or technical contributions, such as peer-reviewed work, validated models, reusable software, datasets, benchmarks, patents, or deployed capabilities. Bring expertise in relevant research topics, such as unsupervised learning, dimensionality reduction, causal inference, survival or longitudinal analysis, Bayesian modeling, multimodal learning, domain adaptation, model interpretability. Are comfortable working with AI agent harnesses, quick to pick up new approaches, and curious about what makes agents useful, reliable, and safe. Have strong Python and scientific-computing skills and experience with a modern ML framework such as PyTorch, JAX, or TensorFlow. Can explain technical findings, tradeoffs, and limitations to clinical, statistical, regulatory, engineering, and leadership audiences. Essentials for the role Hold an MSc or PhD in Computer Science, Machine Learning, Biostatistics, Biomedical Informatics, Computational Biology, or a closely related quantitative field, or have a BSc with substantial equivalent experience. Have independently delivered a meaningful AI or ML research project, workstream, model, evaluation, or software capability from initial question through a clear and reproducible result. Have a record of strong research or technical contributions, such as peer-reviewed work, validated models, reusable software, datasets, benchmarks, patents, or deployed capabilities. Bring expertise in relevant research topics, such as unsupervised learning, dimensionality reduction, causal inference, survival or longitudinal analysis, Bayesian modeling, multimodal learning, domain adaptation, model interpretability. Are comfortable working with AI agent harnesses, quick to pick up new approaches, and curious about what makes agents useful, reliable, and safe. Have strong Python and scientific-computing skills and experience with a modern ML framework such as PyTorch, JAX, or TensorFlow. Can explain technical findings, tradeoffs, and limitations to clinical, statistical, regulatory, engineering, and leadership audiences. Desirables for the role Experience with multimodal biomedical data such as clinical text, EHR or trial data, biomedical imaging, genomics, transcriptomics, spatial biology, or real-world data. Hands-on experience with large language models or agentic systems, including fine-tuning or post-training, retrieval-augmented generation, tool use, agent harnesses, multi-step orchestration, evaluation, or inference and serving. Familiarity with clinical data standards, regulated validation, GxP, software as a medical device, model-risk management, or interactions with health authorities. <span styl