AI Science & Engineering Analyst

AstraZeneca; Gaithersburg (MD); On-site; USD 140,000 - 180,000; Full time

Job summary

AstraZeneca in Gaithersburg, MD is seeking an exceptional Scientist or Senior Scientist to join the AI/ML for Translational and Clinical Sciences team, designing ML systems predicting clinical outcomes, discovering biomarkers, and enabling precision patient stratification across therapeutic areas.

You will build patient-level digital twins by integrating longitudinal clinical, imaging, genomic, proteomic, and real-world data, leveraging foundation models to inform trial design and translational

Qualifications

• PhD in Computer Science, ML, Computational Biology, Biomedical Engineering, Biostatistics, Physics, or related field OR MS with strong applied AI/ML healthcare experience.
• Demonstrated experience building ML/DL models for clinical/biomedical/omics data.
• Strong Python proficiency and experience with ML frameworks (PyTorch, JAX, TF) and distributed training.

Responsibilities

• Design, train, and validate patient-level digital twin models using multimodal longitudinal data.
• Contribute to foundation model research and evaluation for clinical/biomedical data.
• Build predictive and causal ML models for clinical endpoints and outcomes.
• Develop scalable pipelines for integrating structured clinical, omics, imaging, and real-world data.

Skills

Python proficiency

Transformer architectures

Statistical inference

Causal modelling

Survival analysis

Education

PhD in Computer Science / ML / Computational Biology / Biomedical Engineering

Tools

PyTorch

TensorFlow

JAX

Distributed GPU/TPU training

Job description

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 AstraZeneca's therapeutic areas.

You will help build patient-level digital twins that integrate longitudinal clinical, imaging, genomic, proteomic, and real-world data- leveraging 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 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 a