AI Science & Engineering Analyst
AstraZeneca · Gaithersburg, MD
OtherFull-time$72k–$108kCompany career page
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Role summary
AstraZeneca seeks a Scientist or Senior Scientist to develop machine learning systems for clinical outcome prediction, biomarker discovery, and patient stratification. The role combines digital twins, foundation models, and multimodal clinical data integration across oncology, rare diseases, and other therapeutic areas.
What they're looking for
- PhD in computer science, machine learning, computational biology, biomedical engineering, or related quantitative field; or MS with equivalent applied research experience in AI/ML for healthcare
- Demonstrated experience building machine learning or deep learning models on clinical, biomedical, or omics datasets
- Strong Python proficiency and expertise with PyTorch, JAX, or TensorFlow; experience with GPU/TPU distributed training
- Solid understanding of transformer architectures, self-supervised learning, and foundation model training or fine-tuning
- Experience with longitudinal clinical data (EHR, trials, registries) and familiarity with standards such as OMOP, CDISC, FHIR, or DICOM
- Strong foundation in statistical inference, causal modeling, and survival analysis with rigorous validation approach
- Track record of peer-reviewed publications, preprints, or open-source contributions; excellent communication skills
What you'll be doing
- Design and validate patient-level digital twin models simulating disease trajectories and treatment response using multimodal longitudinal clinical data
- Develop and fine-tune foundation models for clinical and biomedical data including EHR, imaging, omics, and clinical notes
- Build predictive and causal models for clinical endpoints, adverse events, disease progression, and treatment response with rigorous external validation
- Create scalable pipelines integrating structured clinical, genomic, proteomic, imaging, and real-world evidence data using representation learning
- Collaborate with clinicians, statisticians, and bioinformaticians to translate models into decision-grade tools; publish findings and represent company at scientific conferences
Privacy & handoff
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