Overview In this role you will define and drive the AI methodology roadmap for Clinical AI programs across early and late phase development. You will lead high-stakes AI projects with scientific authority, shaping enterprise-grade methods and regulatory-aligned solutions. You'll partner across Clinical Development, Biometrics, Regulatory, and Study Teams to embed AI strategy into study design.

This is a high-visibility opportunity to influence how AI is used in bio-pharma clinical development and regulatory engagement.

Responsabilidades

Define and drive the AI methodology roadmap for assigned Clinical AI programmes, aligning priorities with clinical and business objectives

Lead complex AI projects through problem definition, methodology selection, validation, regulatory alignment, and enterprise adoption

Develop and govern reusable enterprise-grade AI methods for clinical trials (design support, dose optimization, biomarker discovery, digital twins, predictive modelling, safety/efficacy signals)

Champion data-centric AI practices at programme level: data acquisition, curation, quality control for training and evaluation

Partner with Clinical Development, Biometrics, Regulatory, and Study Teams to embed AI solutions into study design and decision-making

Shape the AI evidence component for regulatory submissions; act as scientific voice in AI regulatory engagements (FDA, EMA, MHRA)

Evaluate and champion cutting-edge AI methodologies (foundation models, agentic AI, causal inference, multimodal learning, model calibration, domain adaptation)



with robust evaluation and risk assessment

Establish external collaborations with academia, technology partners, and industry consortia to advance scientific agenda

Represent AstraZeneca at scientific conferences and publications; mentor junior scientists to promote rigor and reuse

Contribute to broader AISI AI for Clinical Development strategy including governance and standards

Requisitos principales PhD in a quantitative discipline with hands-on computational track record

4–8 years post-PhD in AI/ML method development with impact in clinical/biomedical settings

Deep experience with biology and biological data (molecular, imaging, or clinical text)

Expertise in modern AI methods (foundation models, Bayesian inference, temporal modelling, multimodal integration, domain adaptation, interpretability)

Exceptional software engineering skills (Python, PyTorch, frontier agent frameworks, LLM tooling, cloud platforms)

Experience translating AI methods into clinical/biomedical decision support with prospective evaluation or regulatory evidence

Track record of scientific influence across ML, clinical, biostatistics, regulatory groups without formal authority

Peer-reviewed publications in clinical AI or ML venues

Excellent written and verbal communication for clinical, regulatory, and executive audiences leadership and matrix collaboration scientific influence and communication ability to translate complex results for diverse audiences

Foundation model training and fine-tuning

Bayesian inference

Temporal/longitudinal modelling

📌 Director, Data Scientist - Clinical AI (Barcelona)
🏢 AstraZeneca
📍 Barcelona