Associate Director, Clinical AI

At AstraZeneca, technology and science meet to change what is possible for patients. We are 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 will actively leverage existing capabilities, celebrate and promote reuse, export breakthrough ideas across geographies and functions, and obsess over scaling impact rather than building in isolation.

AI Science & Innovation (AISI) sits at the centre of 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, biomarkers, and clinical development.

Within AISI, the BioPharma Clinical Development AI team is building world-class AI capability to accelerate the design, conduct, and analysis of clinical trials across our BioPharmaceuticals pipeline - spanning both early and late phase programmes. We partner closely with clinical development, regulatory, and biometrics teams to bring better treatments to patients faster, while adhering to the highest evidentiary standards.

Bringing new treatments to patients demands scientific excellence at every stage of development. In the AI for Clinical Development, BioPharma AIR&D team, we focus on one of the most data-rich and decision-intensive parts of that journey: clinical development. Trial design, patient selection, dose optimisation, biomarker strategy, and safety evaluation each represent genuine opportunities where AI and machine learning can add rigour, speed, and precision - not as a replacement for clinical and statistical expertise, but as a powerful complement to it. We hold ourselves to measurable standards of improvement, and we build methods that can be evaluated, reproduced, and trusted in regulatory settings.

You will work across the enterprise to define and deliver on AstraZeneca's most pressing clinical development questions - collaborating in cross-functional teams spanning the key BioPharmaceuticals disease areas of cardiovascular, renal, metabolic disease, respiratory, and immunology. You and the team will apply new methods to measurably advance the late-stage drug pipeline, and you will help invent reusable approaches that scale across programmes and geographies.

AI for clinical development is a field in motion. Foundation models, agentic systems, and causal AI are advancing rapidly, and the regulatory and methodological frameworks around them are evolving in parallel. As an Associate Director, Data Scientist in the AI for Clinical Developmentteam, you will be hands-on at the frontier - developing, evaluating, and deploying AI methods that directly inform clinical trial design and decision-making across early and late phase programmes. Every model you build will eventually touch a trial that decides whether a patient gets a better therapy. That is the bar we hold ourselves to.

• Develop, evaluate, and deploy reusable AI and machine learning methods for clinical trial settings - including innovative trial design support, dose optimisation, biomarker discovery, digital twins and predictive modelling for early and late phase decisions, and safety and efficacy signal detection.

• Lead end-to-end delivery of data science projects, from problem framing and methodology selection through to implementation, validation, and adoption within study teams.

• Partner with Clinical Development, Study Teams, Biometrics, and Regulatory colleagues to embed AI and analytical strategy into study design and decision-making workflows.

• Build reusable, well-documented data products - including pipelines, packages, and applications - with a software engineering mindset, ensuring quality, reproducibility, and maintainability across the enterprise.

• Apply and evaluate cutting-edge methodologies, including foundation models, agentic AI systems, generative patient models, longitudinal and time-series modelling, Bayesian inference, and causal inference, proposing fit-for-purpose approaches with clear evaluation criteria.

• Drive data-centric AI practices: acquire, curate, and quality-control datasets for model training, post-training, benchmarking, and evaluation in clinical and regulatory settings.

• Contribute to the development of AI evaluation and benchmarking frameworks suitable for clinical and regulatory settings.

• Engage actively with internal data science communities and external scientific forums; contribute to publications and conference presentations as a recognised scientific contributor.

• Provide coaching and technical guidance to Senior Data Scientists and peer colleagues, promoting best practice and a culture of scientific rigour.

• Strong proficiency in augmenting - but not supplanting - daily knowledge work with agentic AI tools.

• Proactively up-to-date with the latest AI research; tries out new tools and methods of interest without waiting to be directed.

• Team-oriented mindset: does what is best for the team and the programme, not just the individual project.

• Ability to deliver high-quality contributions independently and at pace.

• Comfort with ambiguity and an instinct to learn in public, prototype early, and fail forward.