Jesse Levitt

Jesse is a mathematician and data engineer who is passionate about using data to improve lives. Since leaving University of Southern California, where he studied how best to adapt machine learning techniques for knot theoretic data sets, he has focused on developing architectures that enable processing poorly structured data at scale. At a previous health tech startup, he built pipelines to support COVID testing, contact tracing and vaccination work among populations experiencing homelessness. As Data Architect for a geospatial machine learning startup, his work spanned from low level algorithmic improvements to scale to larger and more populace countries to high level systems design to implement a proper MLOps system for data generation and tracking that integrated with their software development cycle. Joining Creyon, he looks forward to using this range of skills to improve experimentation tracking, learning and integration.