AI/ML Scientist - Digital Twins for Precision Medicine

AstraZeneca GmbH; Gaithersburg (MD); On-site; USD 72,000 - 108,000; Full time

Benefits offered by this job

Competitive salary

Performance bonus

Retirement programs

Hybrid working culture

Learning & development budgets

Job summary

AstraZeneca is seeking a Scientist or Senior Scientist to join the AI/ML for Translational and Clinical Sciences team. You will design and build next-generation ML systems to predict outcomes, discover biomarkers, and enable precision stratification across therapeutic areas.

You will develop patient-level digital twins, harness foundation models, and enable decision-grade tools for R&D workflows, collaborating across disciplines and leadership levels.

Qualifications

• PhD in a quantitative field or MS with strong applied AI/ML healthcare experience.
• Experience applying ML to clinical/omics data.
• Strong programming and model development skills in Python.

Responsibilities

• Design digital twin models using longitudinal multimodal data.
• Develop foundation model research for clinical data.
• Build predictive and causal ML models for clinical endpoints.
• Integrate multimodal data across EHR, imaging, omics.
• Lead cross-functional collaborations across QA, translational medicine, and biomarkers.
• Publish in top venues and contribute to strategy.

Skills

Python

Longitudinal clinical data

Survival analysis

Statistical inference

Communication skills

Education

PhD in CS/ML/Computational Biology/Bioengineering/Biostatistics/Physics

MS with equivalent applied AI/ML healthcare experience

Tools

PyTorch

JAX

TensorFlow

Job description

AstraZeneca is seeking a Scientist or Senior Scientist to join the AI/ML for Translational and Clinical Sciences team. You will design and build next-generation ML systems to predict outcomes, discover biomarkers, and enable precision stratification across therapeutic areas.

You will develop patient-level digital twins, harness foundation models, and enable decision-grade tools for R&D workflows, collaborating across disciplines and leadership levels.