Reonomy’s knowledge graph connects the world of commercial real estate; moving paper-heavy, manually collected intelligence into the 21st century.
The resulting breadth and depth of perspective creates an explosion of opportunity for brokers, bankers, roofers, cellular service providers, investors and many more. It levels the playing field in an industry where there has historically been an impossibly high barrier to entry.
All of the components of our knowledge graph have machine learning algorithms embedded in our production pipelines, bringing scalability challenges that have implications which may surprise you – on everything from the algorithms themselves, to the cluster configurations. This talk will cover architectural best practices and how we address these problems at scale.
Maureen Teyssier is Director of Data Science and Data Engineering at Reonomy, a data company which is transforming the world’s largest asset class: commercial real estate.
Maureen has worked to transform data for over 15 years. She has a breadth of knowledge on varieties of data, including: location data, click data, image data, streaming data, and simulated data, as well as experience working with data at scale, managing datasets ranging from kilobytes to terabytes, run on computers with 1-500 cores.
In previous roles, Maureen drove technological and process advancements which resulted in 500% year over year BtoB contract growth at Enigma, a data-as-a-service company headquartered in New York City. She delivered smart technology which anticipates human behavior and needs as a Data Scientist at Axon Vibe, created a smartwatch app recommender as a Data Science Fellow in the Insight Data Science Fellows Program, and studied galactic shapes due to the interplay between Dark Matter and Stellar Evolution as a Post Doctoral Associate at Rutgers University. Maureen’s Ph.D. is in Computational Astrophysics: she studied the evolution of galaxies by running cosmological simulations on supercomputers.