Senior Scientist - Clinical AI

AstraZeneca; Barcelona; On-site; EUR 75,000 - 120,000; Full time

Job summary

AstraZeneca plc in Barcelona, Spain, seeks a hands-on AI/ML specialist to develop and deploy AI methods for clinical trials within the Enterprise AI engine, collaborating with cross-functional teams across BioPharmaceuticals to advance trial design and decision-making.

You will apply foundation models, agentic AI, and Bayesian inference, building reusable data products and scalable AI workflows in a fast-moving, on-site environment.

Qualifications

• Degree in a quantitative discipline at Master's or PhD level or equivalent experience.
• Demonstrated experience in AI/ML method development demonstrated via models, publications, or applied projects in clinical/biomedical settings.
• Hands-on experience with biological or clinical data such as molecular data, imaging, or EHR/text.
• Technical capability in AI areas like foundation models, Bayesian inference, or multimodal integration.
• Strong Python skills, PyTorch, and familiarity with cloud platforms.

Responsibilities

• Develop and deploy AI/ML methods for clinical trials, including design support, dose optimisation, and safety signals.
• Contribute to end-to-end project delivery with guidance from senior colleagues.
• Partner with Clinical Development, Biometrics, and Regulatory to embed AI in study design.
• Build reusable data products, pipelines, and applications with a software mindset.
• Apply modern methodologies (foundation models, agentic AI, causal inference) with clear evaluation criteria.
• Engage with internal/external scientific communities and contribute to publications.

Skills

Python

PyTorch

LLM tooling

Cloud platforms

Biological data

Communication skills

Education

Master's or PhD

Tools

AWS/Azure/GCP

Clinical data handling

Job description

This is an in-office role based in Barcelona, ES, with a requirement to work a minimum of three days per week on-site. Remote or travel flexibility is not available.

About AstraZeneca and AISI

At AstraZeneca, technology and science meet to change what is possible for patients. We are building a connected, end-to-endEnterprise AI enginethat 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 center 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, theBioPharma Clinical Development AI teamis 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 AIare 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 Will Do

• Develop and deploy AI/ML methodsfor 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 initiatives.
• Partner with Clinical Development, Biometrics, and Regulatory colleaguesto embed AI approaches into study design and decision-making.
• Build reusable, well-documented data productssuch as pipelines, packages, and applications, with a software engineering mindset.
• Apply modern methodologiesincluding foundation models, agentic AI, generative patient models, longitudinal modelling, Bayesian inference, and causal inference, with clear evaluation criteria.
• Practise data-centric AIand 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.

What You Will Bring

• 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 experiencein 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.

Nice To Have

• 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 clinical AI, computational drug development, or machine learning.

Why AstraZeneca

We are investing boldly in AI and data to become truly data-led, bringing together unexpected teams to solve problems that have never been solved before. We empower scientists to experiment, prototype early, and partner credibly across ML, clinical, biostatistics, and regulatory communities. We valuelearning agility and technical excellenceequally, and your work will directly influence trials that shape whether patients receive better therapies.

#EAI

Date Posted

29-oct-2026

Closing Date

19-nov-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.