I thought I wanted to be an engineer for a Formula One team (kind of ridiculous in retrospect), so at the time I majored in mechanical engineering. Long story short, I was programming every night instead of doing my mechanical engineering homework, so I eventually decided to leave to find a job building software.

My background is mixed. I was an English major originally, and pivoted to environmental science and biology later in college. In graduate school, I got interested in quantitative / computational ecology and testing ecological theory using historical observational data, experiments, and computer simulations. I worked as an applied ecologist at Northern Arizona University for a couple of years, before co-founding a company called Conservation Science Partners in 2012. We had a tremendous amount of success over the last 10 years, but I was feeling ready for a change in focus.

I am an astronomer by background - I did a PhD in astronomy. I studied the evolution of the galaxy since the Big Bang. Very different from what I do today. My interest in astronomy was motivated by the discovery process. You get to discover something interesting that no one's ever discovered before. But as I was going through that, one thing that I realized - and it led to my transition to data science and ultimately to product - was that I liked working on these sorts of big questions, but I wanted to do so in a bit more of an applied way. 

I started with a design degree from SF State, doing graphic and web design for a small agency. I did a bit of print work as well as creating different kinds of collateral marketing and conferences. 

As I was finishing my PhD, I did Insight Data Science and got hired at Airbnb where I hoped to use geospatial data to work on host growth or something. I was working on the host team as a Data Scientist and I realized that we didn't have any concept of the “context” of our listings… we knew a lot about the listing and its specific characteristics but we didn't really have a way to understand what was nearby. That matters because if you know what’s nearby you can market listings differently (which is sort of what Airbnb is (finally) doing w Categories), you can price more accurately, and you can drastically improve search. That became really compelling to me. I tried to work on an internal project there, but it didn't get a lot of support. So eventually, I decided to leave Airbnb and start Iggy to solve this challenge.

This is a part of a series of posts introducing team Iggy. Meet Alex Malokin, Data Scientist and transportation expert!

This is a part of a series of posts introducing team Iggy. Meet Annie Iezzi, Chief of Staff!

Tune in to hear from Anne about: Motivations for iggy, based on our CEO Lindsay's experience as a data scientist at Airbnb, How we build our core/canonical datasets,Lessons learned from ingesting massive datasets with complex geometries (I.e. FEMA flood data),How iggy can help w health outcomes, Navigating the pre-PMF journey, Why Anne thinks machine learning on geospatial data is particularly cool.

This is the first in a series of posts introducing team Iggy. Meet Anirudh Shah!