# AI Research Lead, AI for Oncology Clinical Development at AstraZeneca

AstraZeneca is hiring an AI Research Lead, AI for Oncology Clinical Development in Barcelona, Spain. Level rates it Builds AI ●●●●; you can [apply on AstraZeneca](https://jobsbylevel.com/go/dd6e2a69-3e5b-4e12-bac5-d1a2e6e68c62?utm_medium=ai-agent).

AI Level 4, AI centrality 100 out of 100. Spain - Barcelona.

## Details

- Company: [AstraZeneca](https://jobsbylevel.com/companies/astrazeneca)
- AI level: AI Level 4 (score 100 out of 100)
- Location: Spain - Barcelona
- Posted: September 28, 2026
- Apply: https://jobsbylevel.com/go/dd6e2a69-3e5b-4e12-bac5-d1a2e6e68c62?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=ai-research-lead-ai-for-oncology-clinical-development-at-astrazeneca-714e5f

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## Description

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. Drug discovery has benefitted enormously in the current AI era, yet comprises only a portion of the journey to bring new treatments to those in need. The final step – clinical drug development – is oft overlooked, despite requiring a significant proportion of time and investment. In the AI for Clinical Development team at AstraZeneca, we're reimagining the process of clinical development. Our vision is to bring safe, efficacious treatments to patients in a way that quantifiably improves our chance to do this faster, more cost-effectively, and with reduced patient burden. 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. Accountabilities 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 Required Qualifications PhD in a quantitative discipline such as computer science, mathematics, physics, biophysics, bioinformatics, computational biology, computational neuroscience, biostatistics 2-5 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 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 Multi-agent systems and agent harnesses Preferred Skills Deep, up-to-date knowledge of ML literature and connections with the ML community (e.g. conference publications, presentations, or collaborations) Experience, knowledge, and understanding of one or more fields of 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

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