Head of Clinical AI Research, AI Clinical Development

This role can be based at AstraZeneca hubs in Boston, US;
Gaithersburg, US;
Cambridge, UK or Barcelona, Spain.

We're building a connected, end-to-end Enterprise AI engine - uniting data foundations, AI technology, process reinvention, and business-facing AI to accelerate results across the whole value chain. Success depends on being exceptional connectors:you'llactivelyleverageexisting capabilities, celebrate and promote reuse, export breakthrough ideas across geographies and functions, and obsess over scaling impact rather than building in isolation. If you thrive in high-collaboration environments where your role is to turn complex, cross-functional problems into reusable, enterprise-wide capabilities - and where the measure of success is adoption and scale, not just innovation - you'll have the platform (and sponsorship) to make it real.

About AISI

AI Science & Innovation (AISI) sits at thecentreof AstraZeneca's R&D AI transformation. Our remit is to build, buy and deliver the AI models and agents that change pipeline outcomes, across discovery, translational science,biomarker sand clinical development.

Role Overview

AstraZeneca is building a world-class AI capability for Clinical Development within the Enterprise AI Unit, with the ambition to fundamentally reimagine how AI supports the way medicines are designed, tested, and brought to patients. We are hiring a Head of Clinical AI Research to set and lead the AI research agenda for this capability and to lead a team of AI/ML researchers working at the very frontier of what AI can do for clinical development.

The mission is to make trial design and delivery AI-native: to target the right treatments to exactly the right patients, to demonstrate benefit with the smallest possible number of patients and the fastest possible trial cycle times, and to raise the probability of success of every program in our pipeline. This is a rare opportunity to help revolutionize clinical development from Phase I through Phase III.

In doing so, you will contribute to getting better medicines to patients faster and advance human health at scale.

This is a research-leadership role at the frontier of the field. You will define and lead the scientific portfolio, including the modeling approaches and methodological standards that our applied and delivery teams build on. You will be accountable for the rigor, originality, and real-world impact of that work. You will translate cutting-edge research into methods that hold up in a regulated setting, and grow a small, exceptional research team that both advances AI methodology and powers portfolio-facing applied AI initiatives that change how our trials are designed and run.

AI for clinical development is a field in motion, and this role sits at its leading edge. Multimodal foundation models, world models, agentic systems, post-training methods, and evaluation science are advancing at extraordinary speed, and the regulatory and methodological frameworks around them are being written in parallel. Because this function is being built from the ground up, you will have rare influence over the research direction, the methods standards, and the shape of the science itself.

You will engage directly with the regulators, scientific consortia, and external partners defining the rules of the road for AI in clinical evidence, where you will help to set them, not simply follow them.

What you'll do

• Set and lead the research agenda for clinical AI methods that reduce the uncertainty, time, and number of patients needed to make confident clinical decisions across Phases I–III, thereby making trial design and delivery AI-native, and prioritizing the modeling problems where original research creates the most value for patients and the pipeline.
• Lead a team of AI/ML researchers developing methods for the highest-value clinical development problems, such as multimodal foundation models, world models and trial simulation, predictive efficacy and safety modeling, patient selection and stratification, causal effects modeling, synthetic control arms, digital endpoints, clinical decision-support over patient data.
• Establish and maintain methodological standards and reusable research artifacts to enable reusability, comparability, and end-to-end integration of AI methodologies.
• Translate research into methods that withstand scrutiny in a regulated environment, working closely with biostatistics, clinical development, translational science, clinical operations, and regulatory…