Overview

In this Associate Director role, you will lead high-impact AI research and engineering to improve oncology trial design, execution, and learnings. You will work at the crossroads of AI, clinical science, and cancer biology, partnering across therapies and general teams to shape questions, build models, and translate them into clinical and regulatory realities. You will deliver strategic AI programs and drive adoption in late-stage development, balancing scientific rigor with real-world impact. This position offers a unique chance to influence trial design and decision-making with cutting-edge methods in a collaborative, patient-focused environment.

Responsabilidades

Co-own and evolve the AI strategy for early and late-phase oncology clinical development, aligning investments and planning from research to adoption Serve as principal technical lead within matrixed teams, delivering high-stakes AI programs on time and to regulatory standards Evaluate and develop advanced AI methods across problem framing, data readiness, governance, and deployment Partner with clinical development, biometrics, regulatory, and study teams to embed AI solutions into study design and decision-making Design rigorous evaluation frameworks,



benchmarking protocols, and calibration plans for real-world clinical use Build and maintain collaborations with academia, technology partners, and industry consortia to advance oncology AI standards Represent AstraZeneca at scientific conferences and publish in leading journals to advance the field Mentor peers, fostering a culture of pragmatic engineering and rapid learning Deliver near-term wins by solving defined study needs and scaling insights into reusable platforms

Requisitos principales

PhD in a quantitative discipline 2-5 years’ post-PhD experience with measurable impact Exceptional software development and coding skills, including frontier coding agent frameworks Deep understanding of ML fundamentals with domain expertise in listed areas Experience with model training, tuning, and calibration Data-centric AI approaches for data collection, curation, benchmarking, and evaluation Model interpretability and alignment for clinical contexts team-oriented mindset ability to work at pace proactive and independent contribution Training and tuning of foundation models Bayesian inference Temporal modeling

📌 AI Research Lead, AI for Oncology Clinical Development (Barcelona)
🏢 AstraZeneca
📍 Barcelona