so I think it was a lot of lot coupled with being curious and excited about interesting technologies. I started off by coming Thio study in the United States from India and, uh, I studied biology, initially moved into computer. Someone's got interested in robotics is what kind of let my interest in machine learning. And after that it was always just need thio legal fit when appropriate. I dropped out of high school to stop all the early and start by bhd and dropping about cto start example where I broke up. Organization. Exactly. It's been a fantastic journey for been support from my peers, and there is also ensuring that I can capitalize on the opportunity. Thio, Giant cage, whatever organization on the bottom as just kind of proved to be very helpful, move things along.

the responsibilities in my particular job right now is any it? Broadly, as I put it, it is deriving information inside from data and making it available for consumption across the organization. So I always think of the field of data analytics machine learning, artificial intelligence as a very kind of service oriented Fiona. When I think service oriented I mean, it's only as powerful as the consumers that I wouldn't do. Leverage later, we provide and, uh, make organization brings with it. So my adaptability examples are search algorithm for the website. Demand Forecasting predicts sizing for customers in shoe price elasticity, profit, intelligent personalization, anything that requires me oh, intelligence. Do made a intelligent piece of information available thio consumers across the order whether it's marketing a traditional experience doesn't matter. Uh, in terms of my literally walk out with, uh, I think it really changes from Thio. Want current fire is and the company. There's always some emergency that we delivered sends our both ends of a regular 9 to 5 work there, but predominantly my kind of spent trying to keep the opposition on being focused on executable and balls and accountability. You may have organization and, uh, what? That's kind of how we it's kind of you

in a job like mine, it's still an emerging technology. Uh, fundamentally speaking. There's not a lot of broad understanding how machine learning and artificial intelligence could be used effectively in a business setting. So a challenge you have to be a very regular basis is being able to translate the mathematical solutions that we come up with into the business problem or that we're trying to tackle with algorithm. For example, examples we don't rate was two way were asked. Is that a exclusion for that? And we kind of dug into it. Identified that there was a problem with side related with customers buying the thing true, returning it and then, you know, buying the same shoe again in different size. And that's, you know, millions of dollars with lines so way both an algorithm to predict the constable styles in every shoe. How does that manifest is an experience on the website? How does that affect the actual return rate? How are we gonna build an environment around it to determine what the actual impact waas? All this becomes very important ingredient to communicate and whether it's in retail in the business setting, I study. I did machine learning. You know, clinical setting is well at Emory, so you know, it always maps back to problem. You're always gonna have a stakeholder that is expected to solve a problem where data And you're the particular that date that data and transported into inside trying to say that into an actionable solution that's gonna solve that problem each step of the way, you're gonna get a translator. I think that's critical.