Director, Data Scientist - Clinical AI

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.

• 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

• 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