Senior Machine Learning Engineer
Joshua D. Hayes, PhD, is a Senior Machine Learning Engineer at Manifold, where he builds machine learning pipelines and deploys products at scale. With over a decade of experience at the intersection of social theory and data-driven analytics, he specializes in translating fuzzy concepts into explicit questions that can be directly connected to data structures and algorithms. Recent product successes include using tools such as scikit-learn and Docker to develop prototypes for a medical device company.
Prior to Manifold, Josh was a Data Scientist at Chartmetric, a data analytics company that tracks the daily digital streaming performance of over 2 million artists. There, he led a team that designed and productionalized machine learning pipelines aggregating data from millions of artists to create a range of tools—including time-series models to predict artist performance, and cluster analyses to help artists identify similar competitors for performance benchmarking. In his doctoral program, he quantified and visualized social-change data across dozens of countries and numerous decades.
Josh holds PhD and MA degrees in Sociology from the University of California, Davis. He has a MA in Religious Studies from Stanford University and a BA in Humanities from Michigan State University. Josh was a Fellow at the The Data Incubator in 2018.