Teams at AstraZeneca

Lead Biologics AI strategy and execution to integrate advanced ML with wet‑lab discovery. Own cross‑functional programs, develop protein/structure‑aware models, establish closed‑loop design‑build‑test‑learn workflows, set data governance, oversee ML lifecycle and deployment, mentor teams, and represent AstraZeneca externally through publications and collaborations.

Develop and maintain end-to-end MS proteomics analysis pipelines, containerized workflows, and data infrastructure. Design proteomics data models, apply FAIR principles, build searchable result databases, and perform proteomics analyses. Collaborate with chemical biology teams to support study design, quality assessment, scalable analytics, and AI-enabled capabilities while establishing software engineering best practices.

Develop and scale automated translational pathology workflows and Python pipelines; apply deep learning and foundation models to whole-slide image analysis; integrate tissue, preclinical, and clinical data; quantify target engagement and pharmacodynamics; produce visualizations and decision-ready outputs; ensure reproducibility, GxP awareness, and cross-disciplinary collaboration to inform oncology indication and dosing strategies.