We build custom algorithms, models, and analyses that best serve our clients’ needs.
We develop and publish new machine learning methods that push the industry forward.
We create tools and products that make machine learning more broadly accessible.
We use data to find new ways of answering questions and building products. Our work includes developing algorithms, building models, and creating tools. If a solution doesn’t exist yet, we’ll invent one.
Here are (just a few) examples:
Our team brings together years of experience building new data algorithms and analyses that actually move the needle.
Tyler has over a decade of experience developing machine learning systems for real-world applications. He built Google’s initial sentiment analysis engine in 2006. He also created robotics simulations for Open AI, developed natural language processing techniques for Primer AI, and co-founded the data science infrastructure company Zillabyte through Y Combinator. Tyler wrote the O’Reilly book Creating Solid APIs with Lua and has three patents focused on sentiment analysis. Tyler holds a Ph.D. in Applied Math, specializing in machine learning algorithms, from NYU.
Mike is the former Head of Data Science at Medium and has eight years of experience using data science to drive growth at technology companies. The team he led at Medium developed recommendation systems, topic classifications, statistical testing tools, and new causal inference techniques. He also created the popular open-source framework Charted. Previously, he led analytics for Adobe’s launch of Creative Cloud and ran supplier optimization projects at Deloitte Consulting. Mike holds a B.S. in Economics from The Wharton School and a B.A. from UPenn.
