Our Start Up blog posts focus on the people that matter in our industry and find out just how they got started in Data Science or in a particular part of the sector.
In this post, we speak to Ryan Kearns from Monte Carlo, one of the world’s leading data reliability companies and ask him what prompted him to pursue a career in Data Science.
Ryan is a founding Data Scientist at Monte Carlo, where he develops machine learning algorithms for the company’s data observability platform. Together with co-founder Barr Moss, he instructed the first-ever course on data observability. Ryan is also currently studying Computer Science and Philosophy at Stanford University.
Towards the end of summer 2020, I was a junior at the University of Stanford during the beginning of the pandemic. I was in school doing research at Stanford in Natural Language Processing when everything started going online. I found that the environment wasn’t really suited to the remote context, so I wanted a break from school while things settled down.
I actually reached out to my mentors at GGV Capital, which is a venture capital firm. I had worked with them previously as an intern doing some analysis work, and I emailed to ask if they knew of anything in the space related to data in AI – the types of things that I had been researching and was familiar with.
They were aware of a company that was doing some really interesting things and was still not overly big. Luckily for me, they were happy to take on someone without a bachelor’s degree at the time, and so I took my chance to jump in and get involved while I waited for school to come back online.
I really expected to be in this game for three to six months to help complete the intern project and then be on my way. But, they put me in touch with Monte Carlo, and told me this company had got really strong validation and their thesis is correct, however, they hadn’t built out too much product yet.
The company was in the process of hiring and scaling that team and so I got involved in September 2020. I was the third Data Scientist, with the other team members coming from Israel. Once we drilled down what my role would be, I took the initiative to build out the distribution part of the platform.
For me, that took the form of metric based anomaly detection; like a time series, anomaly detection. So, I got to work at a great company and – fast-forward a few years- I’m still here. I have become really invested in the team, and I think that the thesis is great. I’ve really had a fantastic time being able to build something pretty cool here.