AI Research Lead, AI for Oncology 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.

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 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

Soft Skills

Strong proficiency in augmenting but not supplanting daily knowledge work with agentic tools

Team-oriented mindset

Ability to proactively and independently deliver high-quality contributions at pace

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

Comfort with ambiguity and a mindset to learn in public, prototype early, and fail forward

Excellent written and verbal communication skills

Why AstraZeneca

Join a company where digital and data are embedded across the full journey from discovery to the clinic, and where AI is used to make trials smarter, faster, and kinder to patients. You will work with clinicians, statisticians, engineers, and product leaders in the same room, turning bold ideas into systems that influence pivotal studies. We value kindness alongside ambition, back experimentation with resources and governance, and equip you with modern tools to push the boundaries of what clinical AI can do. Your contribution will not sit on a shelf; it will inform real decisions, shape a next-generation pipeline, and help reimagine how care reaches patients.

#EAI

Date Posted

03-nov-2026

Closing Date

19-nov-2026

AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.