Tobi has a Ph.D. from Stanford University, and has a background in researching public opinion at the forefront of Machine Learning, leveraging a plethora of disparate data streams: including everything from cell-phone telemetry data to survey data to behavioral news consumption data. Tobi has published his work widely both in the academic and popular press, including in The Washington Post, Slate, and BuzzFeed. His research has been covered by The New York Times and The Atlantic, among others.  Before founding PredictWise, Tobi worked in research and survey science for Facebook.

Gaby is a San Francisco Bay Area native and experienced digital marketing advertising professional. She developed her multifaceted skill set through her experience working in both traditional brand marketing and media strategy. In her past roles she has successfully contributed to large scale digital campaigns for various clients including: Voters Not Politicians, ACLU California, AFSCME, SEIU, Governor & former Presidential candidate Jay Inslee and multiple California ballot propositions. As a first-generation American, daughter of refugee Central American immigrants and native Spanish speaker Gabrielle is proud to be a Latinx in the progressive digital space.

David has a Ph.D. in applied economics from the Wharton School of Business at the University of Pennsylvania. He has written extensively, in both the academic and popular press. His work pushes the boundaries on varying data and methods: polling, prediction markets, social media and online data, and large behavioral and administrative data. His work focuses on solving the most relevant applied questions including: mapping and updating public opinion, the market for news, effect of advertising, finance, and an economist take on public policy.

Jeff Quinn is a data engineer specializing in distributed computing. He has vast managerial experience, and a deep desire for progressive change. Jeff has worked at a variety of early stage technology companies including Outvote.io, BlueVoyant, and NunaHealth.

Nikhil recently graduated with a Ph.D from Stanford University and is a researcher at the intersection of computer science, economics, and operations. He has worked extensively as a data scientist with several online marketplaces and civic engagement platforms, including on the design of survey and rating systems, surge pricing, and how to vote on budgets. He has also analyzed stereotypes in word embeddings and polarization on Twitter, and is focused on solving practical problems in a principled manner.