Rachel Lomasky, PhD, is Director of Machine Learning at Manifold, where she works closely with customers to help them understand how their data fits with their product goals. With an extensive background in both leadership and technology, Rachel is well-versed in building paths from machine learning model to tangible product. Recent product successes include building a modern data warehouse for a payer data platform company and creating a classification model for an analytics software company.
Prior to Manifold, she was co-founder and Chief Data Officer of WEVO Conversion. There, she was responsible for growing the company—from building the engineering team, to defining product strategy, to running investor meetings. Before WEVO Conversion, she was Director of Analytics at Opera Solutions, an AI analytics company. There, she led a 10-person data team, as well as a machine learning and advanced analytics platform. Rachel has also served as an adjunct professor at Northeastern University. Her course “Parallel Processing in MapReduce” covered theory and real-world applications of cloud computing, Hadoop, MapReduce, Hive, Pig, HBase, Spark, as well as other technologies.
Rachel has a BA in Computer Science from Wellesley College, and a PhD in Computer Science from Tufts University, specializing in research on acquiring informative training examples.