Hey there. I'm a data scientist based in New York City. I established the data practice from the ground up at Radicle, a disruption research company, and I'm the founder and chief scientist at Invariant, an applied machine learning product studio. In a previous life I was on MailChimp’s management team, Inc.’s 2017 Company of the Year. My applied work spans computational statistics, statistical inference, natural language processing, and machine learning in all its variants.
I’m the originator of several novel methods for understanding venture capital and the startup economy. My work has appeared in The Wall Street Journal, Crunchbase News, Hacker Noon, and Towards Data Science, and has been cited by hundreds of startup accelerators as well as industry leaders at places like Kleiner Perkins, Silicon Valley Bank, and Deutsche Telekom—in the process redefining how entrepreneurs around the world assess their cash runway, think about startup failure rates, and use empirical data to estimate startup valuations.
I conceptualize, design, and build the systems that power Radicle's future-focused research and discovery products, including Startup Anomaly Detection™, a state of the art algorithm which estimates the plausibility that a startup will have a liquidity event such as an IPO or acquisition. I also conceptualized the Capital Concentration Index™, a measure of startup competition, figured out a way to measure the velocity and magnitude by which a startup sector is raising capital, and conjured up the Investor Cluster Score™, a measure of the signal generated by the investors in a startup’s capitalization table. Much of my time now is spent focusing on building the natural language engines that feed our Disruption Discovery Platform™.
I’m concurrently building Invariant, an applied machine learning product studio.
Read my latest work on Medium, and don't hesitate to reach out if you'd like to say hello.