Senior Scientist - Clinical AI

About this job

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 that unites data foundations, AI technology, process reinvention, and business-facing AI to accelerate results across the whole value chain. Success depends on being an exceptional connector: you will leverage existing capabilities, promote reuse, share breakthrough ideas across geographies and functions, and focus on 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, in 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 Clinical development is one of the most data-rich and decision-intensive parts of bringing a new medicine to patients. Trial design, patient selection, dose optimisation, biomarker strategy, and safety evaluation are all areas where AI and machine learning can add rigour, speed, and precision, as a powerful complement to clinical and statistical expertise. You will work in cross-functional teams across the key BioPharmaceuticals disease areas of cardiovascular, renal, metabolic disease, respiratory, and immunology. Alongside experienced data scientists and clinical collaborators, you will apply modern AI methods to measurably advance the drug pipeline and help build reusable approaches that scale across programmes. Foundation models, agentic systems, and causal AI are advancing rapidly, and the regulatory and methodological frameworks around them are evolving in parallel. In this role you will be hands-on at the frontier, developing, evaluating, and deploying AI methods that directly inform clinical trial design and decision-making. Every model you contribute to will eventually touch a trial that decides whether a patient gets a better therapy. That is the bar we hold ourselves to. What You'll Do * Develop and deploy AI/ML methods for clinical trials, including trial design support, dose optimisation, biomarker discovery, digital twins, predictive modelling, and safety and efficacy signal detection * Contribute to end-to-end project delivery, from problem framing and methodology selection through to validation and adoption within study teams, with guidance from senior colleagues on more complex initiative. * Partner with Clinical Development, Biometrics, and Regulatory colleagues to embed AI approaches into study design and decision-making * Build reusable, well-documented data products such as pipelines, packages, and applications, with a software engineering mindset * Apply modern methodologies including foundation models, agentic AI, generative patient models, longitudinal modelling, Bayesian inference, and causal inference, with clear evaluation criteria * Practise data-centric AI and contribute to evaluation and benchmarking frameworks suitable for clinical and regulatory settings * Engage with internal and external scientific communities, contributing to publications and conference presentations Essential Requirements * A degree in a quantitative discipline (e.g. Computer Science, Machine Learning, Statistics, Mathematics, Biomedical Informatics, Computational Biology, Physics, Engineering) at Master's, or PhD level, or equivalent experience * Demonstrated experience in AI/ML method development, shown through delivered models, publications, open-source contributions, or applied projects, ideally in clinical, biomedical, or drug development settings * Hands-on experience with biological or clinical data, such as molecular (DNA, RNA, protein), imaging, or clinical text (EHR, clinical notes) * Technical capability in one or more modern AI areas, such as foundation model training and fine-tuning, Bayesian inference, temporal and longitudinal modelling, multimodal integration, model interpretability, or post-training and alignment * Strong Python skills, experience with PyTorch and modern LLM tooling, and familiarity with cloud platforms (e.g. AWS, Azure, GCP) * Excellent communication skills, with the ability to translate complex methods into actionable insights for clinical, regulatory, and scientific audiences Desirable Skills and Experience * Exposure to clinical development, such as trial design, biomarkers, dosing, safety, endpoints, or regulatory processes * Experience with MLOps/LLMOps, uncertainty quantification, or clinical AI benchmarking * Peer-reviewed publications in clin

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