Daisy Omondi is an applied data scientist with a healthcare background who works on real-world AI systems where data quality, assumptions, and design choices directly affect human outcomes.

Ms. Omondi speaks about building and evaluating AI systems in high-stakes contexts, translating responsible AI principles into concrete design decisions, and bridging the gap between data science theory and real-world deployment. Her sessions are aimed at data scientists, engineers, and technical decision-makers working on applied AI systems.

Earlier in her career, Ms. Omondi worked in healthcare as a nurse, an experience that shaped her perspective on ethics, accountability, and the real-world consequences of decisions made under uncertainty. Having lived and worked across different systems in Africa and Europe, she brings a global lens to how data-driven technologies include or exclude different communities.

Ms. Omondi has presented academic work at international forums and regularly writes about technology, data, and social impact. Her work focuses on how data quality, assumptions, and system design choices shape outcomes in real-world AI deployments, with a particular interest in transparency, accountability, and trust.