Summary
David Healey is a data scientist and machine-learning leader with a decade of experience applying deep neural networks, computer vision, NLP, and graph models to life sciences problems. With a PhD from MIT's Center for the Physics of Living Systems, he has blended rigorous theory in evolutionary and population dynamics with hands-on drug-discovery work—contributing to multiple clinical-stage candidates during his time at Recursion. At Enveda he scaled ML for molecule structure elucidation from mass spectra and reasoning over large biomedical knowledge graphs to accelerate natural product lead generation. He has also taught deep learning for healthcare, consulted across biotech and conservation domains, and led interdisciplinary teams that bridge experimental biology and computational modeling. Notably, his background in stochastic gene expression and social decision-making gives him a distinctive ability to translate complex, noisy biological signals into predictive models with real-world impact.
11 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Evolutionary systems social decisionmaking population dynamics, Doctor of Philosophy (Ph.D.) Evolutionary systems social decisionmaking population dynamics at Massachusetts Institute of Technology
BS Biochemistry, BS Biochemistry at Brigham Young University
Spanish