Locations: Cambridge, UK or Barcelona, Spain Salary & Benefits: Competitive
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
Clinical AI team are
building world-class AI capability to accelerate the design, conduct, and analysis of clinical trials across our
BioPharmaceuticals
pipelines
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
Clinical AI 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 — leading cross-functional teams
spanning the key
BioPharmaceuticals
disease areas of cardiovascular, renal, metabolic disease, respiratory,
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The salary is an estimate from national earnings data, not the employer's figure.
Why
In favour: Licensed for Skilled Worker
AstraZeneca UK Limited is on the register with an active Skilled Worker route (Worker (A rating)).
Home Office register of licensed sponsors
In favour: Occupation is eligible for Skilled Worker
Occupation code 2433 (Actuaries, economists and statisticians) is eligible for the Skilled Worker route on the rules in force from 11 Nov 2025.
Immigration Rules Appendix Skilled Occupations
In favour: Estimated salary clears the going rate
No salary is stated, so this uses an estimate of £65,960 from ASHE estimate (low end of the principal band) (1 Apr 2025). It clears the going rate for occupation code 2433 of £55,100 by £10,860. Because that salary is estimated rather than stated, this rule counts half.
Immigration Rules Appendix Skilled Occupations
In favour: A rating on the licence
The register shows “Worker (A rating)”, the rating a licence carries when it is not on an action plan.
Home Office register of licensed sponsors
In favour: A larger employer
Companies House records accounts of category “FULL”, filed by companies above the small-company thresholds.
Companies House
Not established: The advert says nothing about sponsorship
We searched the advert for the usual wording in both directions and found none of it.
The advert itself
Home Office register of licensed sponsors snapshot
Information from public records, not immigration advice.
immunology and cell-therapy . You and the team will develop reusable methods and enterprise-scale approaches that measurably advance the late-stage drug pipeline. This is a high-visibility opportunity to shape how AstraZeneca does AI for
BioPharmaceuticals
clinical development — from
methodology
standards to external scientific influence.
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 a Director, Data Scientist, you will define and drive the AI
methodology
agenda for one or more
programmes
within
Clinical AI, leading by scientific influence and matrix coordination rather than through a formal hierarchy. You will be the scientific authority that study teams, biometrics, and regulatory colleagues turn to — and AstraZeneca's voice externally at the critical moment when the rules of the road for AI in clinical trials are being written.
Key Responsibilities
Define and drive the AI
methodology
roadmap for assigned
Clinical AI
programmes , spanning early and late phase clinical development, and aligning AI/ML priorities with clinical and business
objectives .
Lead, by matrix influence and scientific authority, the delivery of the most complex and high-stakes AI projects — from problem definition and
methodology
selection
through validation, regulatory alignment, and scaled adoption across the enterprise.
Develop and govern reusable, enterprise-grade AI methods and evaluation frameworks for clinical trial settings, including innovative trial design support, dose
optimisation , biomarker discovery, digital twins, predictive and prognostic modelling, and safety and efficacy signal detection.
Champion data-centric AI practices at
programme
level: govern the acquisition, curation, and quality control of datasets for model training, post-training, benchmarking, and evaluation across clinical and regulatory settings.
Partner with Clinical Development, Biometrics, Regulatory, and Study Teams to embed AI strategy and
validated
solutions into study design and decision-making at
programme
level.
Shape the AI evidence
component
for regulatory submission packages; act as the scientific and methodological voice in regulatory engagements involving AI/ML methods or innovative trial designs (FDA, EMA, MHRA).
Evaluate and champion
cutting-edge
AI methodologies — including foundation models, agentic AI systems, generative patient models, multimodal learning, Bayesian inference, causal inference, and model calibration and domain adaptation — proposing fit-for-purpose approaches with robust evaluation criteria and risk assessment.
Establish and
maintain
external collaborations with academic institutions, technology partners, and industry consortia to access novel capabilities and advance the scientific agenda.
Represent AstraZeneca at scientific conferences, standards bodies, and peer-reviewed venues;
contribute
first- or last-author publications in leading ML and clinical AI journals.
Serve as a technical mentor and thought partner for Associate Directors and Senior Data Scientists within the Clinical AI team; promote scientific
rigour , reuse, and a culture of learning in public.
Contribute to the broader AISI AI for Clinical Development strategy, including cross-functional ways of working, tooling governance, and
methodology
standards.
Essential Requirements
PhD in Computer Science, Machine Learning, Statistics, Mathematics, Biomedical Informatics, Computational Biology, or a closely related quantitative discipline — with a strong, hands-on computational
track record .
4–8 years of post-PhD experience in AI and machine learning method development, with demonstrated and sustained impact in clinical, biomedical, or drug development settings ( e.g.
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
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 engineering skills: Python, deep learning frameworks ( e.g.
PyTorch ), frontier coding agent frameworks, modern LLM tooling, and cloud platforms ( e.g.
AWS, Azure, GCP).
Demonstrated experience translating AI methods into applications that inform clinical and/or biomedical decisions, including prospective evaluation or contribution to submission-relevant evidence.
Track
record of
driving scientific influence across cross-functional communities — ML, clinical, biostatistics, regulatory — without relying on formal authority.
Peer-reviewed publications in clinical AI, computational drug development, or leading ML venues ( e.g.
NeurIPS , ICML, ICLR, Nature Medicine, Lancet Digital Health).
Excellent written and verbal communication skills, with
demonstrated
ability to translate technical findings for clinical, regulatory, and executive audiences.
Desirable Skills and Experience
Direct industry experience in early or late phase
BioPharmaceuticals
clinical development, including clinical trial design, biomarker discovery, companion diagnostics, dosing, safety, endpoints, or regulatory processes.
Direct experience contributing to FDA, EMA, PMDA, or MHRA submissions involving AI/ML methods or innovative trial designs.
Prior
regulatory engagement on AI methodology, complex innovative trial design, or AI/ML qualification opinions.
Experience with
MLOps
and
LLMOps
at scale, including CI/CD pipelines and enterprise deployment.
Open-source contributions, workshop
organisation , or standards-body participation.
Knowledge of computing hardware and its impact on model training and inference at scale.
Experience in a complex global
organisation
spanning multiple sites and therapy areas.
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 deliverable.
Ability to deliver high-quality, high-impact contributions independently and at
pace .
Comfort with ambiguity and an instinct to learn in public, prototype early, and fail forward.
Deep, up-to-date knowledge of the ML literature and active connections with the ML community.
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. When we put unexpected teams in the same room, we ignite bold thinking with the power to inspire life-changing medicines.
The playbook for AI in clinical development will be written in the next two to three years. You will help write it — with an outsized voice at regulatory agencies, scientific consortia, and external partners during the narrow window when the rules of the road for AI in pivotal evidence are being defined. That is the reason
to come .
We hire for learning agility and technical excellence. The strongest candidates here learn fast, are comfortable with ambiguity, prototype early, fail forward, and partner credibly across communities. We balance the expectation of being in the office — on average
at least
three days per week — while respecting individual flexibility.
So, What's Next?
Are you ready to
set
the AI
methodology
agenda for Late BioPharma clinical development at one of the world's leading biopharmaceutical companies? Submit your CV and cover letter and let us explore how your
expertise
can help AstraZeneca harness AI to deliver life-changing medicines to the patients who need them most.
Date Posted 10-sep.-2026 Closing Date 29-sep.-2026 Our mission is to build an inclusive and equitable environment. We want people to feel they belong at AstraZeneca and Alexion, starting with our recruitment process. We welcome and consider applications from all qualified candidates, regardless of characteristics. We offer reasonable adjustments/accommodations to help all candidates to perform at their best. If you have a need for any adjustments/accommodations, please complete the section in the application form.
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