AI Science & Engineering Analyst

AstraZeneca is a global science-led patient-focused biopharmaceutical company that focuses on the discovery development and commercialisation of prescription medicines in Oncology Rare Diseases and BioPharmaceuticals including Cardiovascular Renal & Metabolism and Respiratory & Immunology. We are committed to pushing the boundaries of science to deliver life-changing medicines and we believe that data science and artificial intelligence are central to how we will redefine drug discovery and patient care over the next decade.

About the Role

We are looking for an exceptional Scientist or Senior Scientist to join our growing AI/ML for Translational and Clinical Sciences team working at the intersection of digital twins foundation models and multimodal clinical data. In this role you will design and build next-generation machine learning systems that predict clinical outcomes discover novel biomarkers and enable precision patient stratification across AstraZenecas therapeutic areas.

You will help build patient-level digital twins that integrate longitudinal clinical imaging genomic proteomic and real-world dataleveraging foundation models to reason across modalities and time. Your work will directly inform trial design endpoint selection and translational decision-making ultimately accelerating the delivery of transformative therapies to patients.

This is a highly collaborative role sitting at the interface of Data Science Clinical Development Translational Medicine and Biometrics with strong exposure to therapeutic area leadership.

What Youll Do

• Digital Twin Development: Design train and validate patient-level digital twin models that simulate disease trajectories and treatment response using longitudinal multimodal clinical data.

• Foundation Model Research: Contribute to the development fine-tuning and evaluation of foundation models (transformer-based generative and multimodal) tailored to clinical and biomedical data including EHR medical imaging omics and free-text clinical notes.

• Clinical Outcome Prediction: Build predictive and causal ML models for clinical endpoints adverse events disease progression and treatment response ensuring rigorous validation against prospective and external datasets.

• Multimodal Data Integration: Develop scalable pipelines and representation-learning approaches that unify structured clinical genomic transcriptomic proteomic imaging and real-world evidence data.

• Biomarker Discovery: Apply interpretable ML and causal inference methods to identify and validate novel prognostic and predictive biomarkers from clinical trial and real-world datasets.

• Patient Stratification: Design ML-driven stratification strategies to support precision medicine hypotheses enrichment trial designs and companion diagnostic development.

• Cross-Functional Collaboration: Partner closely with clinicians statisticians translational scientists bioinformaticians and MLOps engineers to translate models into decision-grade tools embedded in R&D workflows.

• Scientific Leadership: Publish in top-tier venues (Nature Medicine NeurIPS ICML Cell Patterns Lancet Digital Health) represent AstraZeneca at external conferences and contribute to strategic partnerships with academic and technology collaborators.

• Regulatory & Ethical Rigour: Ensure that models are developed in line with GxP model risk management fairness privacy and emerging regulatory guidance (FDA EMA MHRA) for AI/ML in drug development.

At the Senior Scientist level you will additionally be expected to shape scientific strategy mentor junior scientists lead cross-functional workstreams and act as a technical authority in digital twin and foundation model methodology across the portfolio.

Essential Requirements

• PhD in Computer Science Machine Learning Computational Biology Biomedical Engineering Biostatistics Physics or a closely related quantitative discipline OR an MS in a comparable discipline with equivalent applied research experience in AI/ML for healthcare or life sciences.

• Demonstrable experience developing machine learning or deep learning models applied to clinical biomedical or omics data.

• Strong proficiency in Python and modern ML frameworks (PyTorch JAX or TensorFlow) including experience with distributed training on GPU/TPU infrastructure.

• Solid understanding of transformer architectures self-supervised learning and foundation model training or fine-tuning paradigms.

• Experience working with longitudinal clinical data (EHR clinical trials registries) and familiarity with data standards such as OMOP CDISC (SDTM/ADaM) FHIR or DICOM.

• Strong grounding in statistical inference causal modelling or survival analysis and a rigorous approach to validation and generalisation.

• Track record of scientific output through peer-reviewed publications preprints or open-source contributions.

• Excellent written and verbal communication skills with the ability to explain complex methods to non-technical stakeholders.

Experience expectations by level:

• Scientist: PhD with 03 years of relevant post-PhD experience or MS with 4 years of relevant industry/research experience in applied ML for biomedical data.

• Senior Scientist: PhD with 5 years of relevant post-PhD experience or MS with 8 years of relevant experience and a demonstrated record of leading end-to-end ML projects and influencing scientific or product strategy.

Desirable Requirements

• Experience building or contributing to digital twin synthetic control or mechanistic-ML hybridmodels in a healthcare or life-sciences setting.

• Familiarity with multimodal representation learning including vision-language models graph neural networks or time-series transformers.

• Prior work with multi-omics integration (genomics transcriptomics proteomics single-cell) and pathway-informed modelling.

• Experience deploying models in regulated environments (GxP SaMD) and familiarity with model interpretability uncertainty quantification and fairness frameworks.

• Exposure to cloud platforms (AWS Azure GCP) MLOps tooling and reproducible research practices (containers workflow managers experiment tracking).

• Experience collaborating within pharma R&D clinical development or academic medical centres.

Why AstraZeneca

At AstraZeneca we are pioneering a new era of scientific discovery where data AI and human ingenuity converge to redefine what medicine can do. Our AI/ML community spans thousands of scientists and engineers working across the entire value chainfrom target discovery to real-world evidencesupported by world-class compute curated clinical datasets and deep therapeutic expertise. You will have the rare opportunity to see your models move from prototype to production influencing decisions that shape clinical trials and reach patients globally.

We offer a competitive salary performance bonus share programmes comprehensive health benefits generous parental leave learning and development budgets and a strongly hybrid inclusive working culture.

Diversity & Inclusion

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. All qualified applicants will receive consideration for employment without regard to race colour religion sex sexual orientation gender identity national origin disability or protected veteran status.

How to Apply

Please submit your CV together with a short cover letter outlining your relevant experience and motivation for the role. Applications will be reviewed on a rolling basis.

Date Posted

Closing Date

Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and furtherance of that mission we welcome and consider applications from all qualified candidates regardless of their protected characteristics. If you have a disability or special need that requires accommodation please complete the corresponding section in the application form.