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 AstraZeneca's therapeutic areas.

You will help build patient-level digital twins that integrate longitudinal clinical, imaging, genomic, proteomic, and real-world data—leveragingfoundation 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 You'll 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 0–3 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 chain—from target discovery to real-world evidence—supported 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.