Associate Director, Clinical AI

About AstraZeneca and AISI

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.

The Opportunity

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 Development team, 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.

Key Responsibilities

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.

Essential Requirements

PhD preferred; MSc with an exceptional computational track record considered. Disciplines: Computer Science, Machine Learning, Statistics, Mathematics, Biomedical Informatics, Computational Biology, or a closely related quantitative field.

2–5 years of post-PhD (or equivalent) experience in AI and machine learning method development, with demonstrated impact in clinical, biomedical, or drug development settings (e.g. models delivered, first-author publications, open-source contributions, SaMD filings).

Deep experience, knowledge, and understanding of one or more fields of biology, with hands-on experience working with biological data such as molecular (DNA, RNA, protein), imaging (radiology, microscopy), or clinical text (EHR, clinical notes).

Deep technical expertise in modern AI methodologies, including one or more of: foundation model training and fine-tuning; Bayesian inference; temporal and longitudinal modelling; multimodal integration; model calibration and domain adaptation; data-centric AI; model interpretability; model post-training and alignment.

Exceptional software development skills in Python, leveraging frontier coding agent frameworks; experience with deep learning frameworks (e.g. PyTorch) and modern LLM tooling.

Demonstrated experience building and deploying robust, reusable analytical solutions, including familiarity with cloud platforms (e.g. AWS, Azure, GCP).

Proven track record of translating complex methodologies into actionable insights and embedding solutions within cross-functional teams.

Excellent written and verbal communication skills, with the ability to convey technical findings clearly to clinical, regulatory, and scientific audiences.

Desirable Skills and Experience

Experience in early or late phase pharmaceutical or clinical development, including clinical trial design, biomarker discovery, companion diagnostics, dosing, safety, endpoints, or regulatory processes.

Experience with clinical AI evaluation and benchmarking in prospective or submission-relevant settings.

Knowledge of uncertainty quantification and model interpretability methods.

Experience with MLOps or LLMOps and CI/CD pipelines.

Experience with multimodal data integration across molecular, imaging, and clinical modalities.

Peer-reviewed publications in clinical AI, computational drug development, or machine learning venues.

Knowledge of computing hardware and its impact on model training and inference.

Experience working in complex global organisations.

Soft Skills

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.

Why AstraZeneca?

Here, technology and science meet to change what is possible for patients. You will join a company investing boldly in AI and data to become truly data-led, where unexpected teams come together to address problems that have never been solved before. We empower scientists to experiment, prototype early, fail forward, and partner credibly across communities, ML, clinical, biostatistics, regulatory.

We value learning agility and technical excellence equally. The strongest contributors here are those who learn fast, are comfortable with ambiguity, and obsess over real-world impact rather than building in isolation. Your work will directly influence trials that shape whether patients receive better therapies.

We balance the expectation of being in the office, on average at least three days per week, while respecting individual flexibility. Join us in our unique and ambitious world.

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.

So, What's Next?

Are you ready to build AI that matters, at the intersection of machine learning, clinical science, and real patient impact? Submit your CV and cover letter and let us explore how your expertise can help AstraZeneca accelerate the next wave of breakthrough medicines.

Find out more at: https://careers.astrazeneca.com/...

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#EAI

Date Posted

25-sept-2026

Closing Date

05-oct-2026

AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.