Daniel Odonovan
Principal Machine Learning Engineer @Healx
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WORK HISTORY
Principal Machine Learning Engineer @Healx
Cambridge, GB
Designed and delivered machine learning systems that integrate biomedical literature, knowledge graphs, and large-scale datasets to accelerate rare disease drug discovery.• Developed models that prioritised therapeutic candidates, directly shaping internal research strategy and downstream biological validation.• Partnered with biologists, data scientists, and clinicians to translate complex model outputs into actionable research decisions, ensuring alignment between technical approaches and clinical needs.• Led cross-functional initiatives on knowledge graph reasoning, scalable data integration, and predictive modelling — enabling more efficient identification of disease–drug associations.• Contributed to the organisation’s strategic thinking on AI in healthcare, including approaches to data governance, interpretability, and responsible model deployment.
EDUCATION
University of Warwick
Bachelor of Science (B.Sc.), Physics and Mathematics
Trinity College Dublin
Master of Science (M.Sc.), High Performance Computing
University of Cambridge
Doctor of Philosophy (Ph.D.), Biochemistry
SKILLS
ABOUT DANIEL ODONOVAN
I’m a Principal Machine Learning Engineer working at the intersection of AI, biomedical data, and rare disease drug discovery. My work focuses on applying large-scale data integration and graph-based ML to generate insights that accelerate research pipelines and inform clinical and strategic decisions.I have a track record of building and deploying machine learning systems that connect complex biomedical knowledge with practical outcomes — from prioritising candidate therapeutics to supporting multi-disciplinary teams in target discovery. A core part of my role is translating between technical, biological, and clinical perspectives so that advanced models can be turned into actionable strategies.Alongside technical delivery, I’m increasingly interested in the wider context of AI in healthcare: ethical use of patient data, regulatory considerations, and the ways AI/ML can support sustainable models for discovery and care. I’ve collaborated across scientific, clinical, and commercial functions, and I value cross-sector partnerships that move the field forward.I’m always open to engaging more broadly with the community — whether that’s through contributing to panels, advisory groups, grant review boards, or collaborative initiatives around AI in healthcare.
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