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.
Are you ready to harness advanced AI to redesign how oncology trials are conceived, executed, and learned from? In this Associate Director role, you will lead high-impact AI research and engineering that reduces patient burden, increases trial efficiency, and sharpens decision-making in late-stage development. Your work will directly influence study design, dosing strategies, endpoints, and safety evaluations, accelerating the delivery of safe, effective medicines to people with cancer.
You will operate at the intersection of AI, clinical science, and cancer biology, partnering across hematology, cell therapy, antibody-drug conjugates, small molecules, and biologics. Based in Barcelona, you will collaborate with global teams to define the questions that matter most, build models that answer them, and translate those models into clinical and regulatory realities. What could you achieve if your models were deployed where decisions are made?
Accountabilities
AI Strategy and Roadmap: Co-own and evolve the AI strategy for early and late-phase oncology clinical development, aligning investments to the highest-value opportunities and setting a clear path from research to adoption
Technical Leadership: Serve as the principal technical lead within matrixed teams, delivering complex, high-stakes AI programs on time and to a standard that withstands scientific and regulatory scrutiny
Method Innovation: Evaluate and develop cutting-edge AI methods across problem framing, data readiness, governance, algorithm development, validation, and deployment; select the right tool for the right question
Clinical Partnership: Partner with clinical development, biometrics, regulatory, and study teams to embed novel AI solutions into study design, operational execution, portfolio strategy, and go/no-go decision-making
Evidence and Validation: Design rigorous evaluation frameworks, benchmarking protocols, and calibration plans to ensure models are reliable, interpretable, and fit for purpose in real-world clinical contexts
External Ecosystem: Build and maintain collaborations with leading academic groups, technology partners, and industry consortia to access novel capabilities and shape standards that matter to oncology development
Scientific Leadership: Represent AstraZeneca at scientific conferences and standards bodies; author first- or last-author publications in leading ML and clinical AI journals to advance the field and our influence
Team Development: Mentor and support peers, fostering a culture of curiosity, pragmatic engineering, fast prototyping, and learning in public
Impact Progression: Deliver near-term wins by solving defined study and program needs; scale insights into reusable platforms and playbooks that raise the bar across the portfolio
Essential Skills/Experience
PhD in a quantitative discipline such as computer science, bioinformatics, computational biology, mathematics, physics, biophysics, 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)
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
Desirable Skills/Experience
Deep expertise in cancer 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
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
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
28-sept-2026
Closing Date
11-oct-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.