I mean for most people that's a winding tale. So I started out in college as an undergraduate, couldn't figure out what I wanted to do. I had seven different majors and ended up as a philosophy major. Still, at that point, I didn't know what I wanted to do, I finished up my undergraduate to create a career and there aren't many jobs available for philosophy majors. I ended up working alongside high school students. For my first job, it was pretty demoralizing. Eventually, I ended up in the job, working alongside a guy who was doing his bachelor's degree in statistics and I give a hard time about that because nothing sounded less interesting to me than statistics. But he said, "you should look into it, it's not what you think it is" and so I did and the more I looked into it, the more it sounded appealing to be. So I talked my way into a master's program in statistics and from it was kind of a whirlwind. I did that in a year, I felt like I really didn't know much after a year but I had professors who really believed in me and saw something in me that I didn't see it myself. So after working on that master's degree for a year, I went off in the Ph.D. in Biostatistics at Harvard University and so from there, it was a struggle for me because I felt like I was lacking a lot of background knowledge but you work your way through and you work your tail off and then find a way to make it work. Then from there, I ended up in academia. I spent 10 years as a professor. I really enjoyed that and a lot of people just stay there for their whole career. So I had 10 years, I could've stayed there my whole career, but I've always been motivated by progression and by challenges. I just felt like after 10 years, I was ready for a new challenge, looking for some new things and so I ended up working for the health care system. I've done medical research during my time as a professor and did end up in the health care system, did analytics for them did that for a few years and that's kind of just as past fall I ended up working for a machine learning and artificial intelligence, really got a platform machine learning platform, and that's where I am now. In terms of what shaped my experiences along the way, I guess you know two main things one would be, just kind of figuring out where you fit in the world, where everybody has given skill set and kind of figuring out for yourself where you best fit in. I think the second thing is the people who helped me along the way, the relationships I established as a student and then in my various roles, you establish trust with people and they get to know who you are and that just opens up doors down the road and that's kind of where all my opportunities have come from, probably from the combination of those two things.

With any startup, things are going to be in flux. I mean, working for a large corporation, you're going to have a very defined role, and it's probably relatively narrow in scope and so working for H2O, I think they're still classified as a relatively new startup. They've got about 200 employees now and they're quickly expanding, but they're still at a start-up stage, they are in the series defunding now but they're working on growing rapidly. But because of that whatever role I have there is kind of in flux. I wear a couple of different hats, one is, I mean, director Customer success, I work directly with customers, helping them implement H2O based solutions in their data science pipeline. So it's good customer service skills, focus on the customer, listening to what they need, having technical expertise behind that so that I could go in and really help them with their technical challenges. Then on top of that, I spend time developing training materials for customers so that they can get up to speed on the products themselves. Then on top of that based on healthcare background, they're having me kind of build out their healthcare vertical. So building out the tools and data examples that are going to help expand our footprint in health care with healthcare customers. So I kind of split my time between those three areas. A lot of my time is actually spent working from home and then I'm also on the road visiting customer sights, visiting headquarters and but I probably don't do any more than maybe two or maximum of three trips a month at each one of those is two or three days so not an excessive amount of time traveling but there's some travel involved.

I guess for the past 15 years R and python have just been my bread and butter. So I spent most of my time either R or on Python. Most weeks I'm in both and I've done a variety of things in both R and Python. In addition to just kind of base R, I've done a lot with kind of graphical tools in R, R Shiny, Plotly, those sorts of interactive tools building a lot of dashboards, especially in my last job. Now it's lesser with the graphical interfaces or developing graphical tools for end-users and more just on the technical side of R and Python, building out data solutions in both. So within each of those, just data wrangling, a lot of just understanding different ways of wrangling data within each, whether it's a data table, whether it's Tidyverse R, whether it's Pandas, whether it's a data table in python these are various tools for that, and then within each as well, what kind of learning, the machine learning platforms that are available and then right now, I'm focused mainly on the tools provided by H2O, the open-source tools provided by H20 and then the just graphical tools as well in each. On top of that based on my current role, I spent a lot of time in the H2O Enterprise product, called Driverless AI which is a stand-alone product. It does have Python and R interfaces, but it also has a graphical user interface front end. Then just the basic tools of data scientists, SQL. I think that basically sums it up. There are other things that come into play based on which customer I'm dealing with and what their data environment is whether it's just a distributed data environment, whether it's a cloud environment, they are using AWS or a docker Environment. So I don't spend the bulk of my time there, but I need to understand the kind of those varying data environments in order to be able to help customers.