Okay, so I am a data scientist. I've been I graduated two years ago, and, um, I have never I didn't have a thought that I would be in this career path when I started college is wasn't something that I had seen or heard of. It was really a world that I discovered during college happened through taking, um, courses in computer science and statistics. I took a computer science course and on the one and it was very there was a unique experience that I had taken a computer science course just running, ah, for Lupus, them before they've been seeing how powerful computers there is a really unique experience. I didn't think I'd had that anywhere else for that. Um, but otherwise, I didn't find computer science. So a line to the way that I like thinking. I found it a little more engineering focused, and I'm not so engineering focused. But I found much more alignment with studying statistics. I felt statistics. I found really powerful framework for conceptualizing data and processing data. So that's kind of the area of study that I've really enjoyed. But I found that continuing to pursue computer science brought a certain power to that way of thinking and then combination. That was a really, um, amazing skill set. So you pursuing those two things in tandem? One that I didn't love doing, But I saw the potential in and one that I really enjoyed. Um, when they came together, I thought it was like a really exciting career path to follow. Um, And then in terms of how I got a job at IBM, which was my first college, I, um I have always had a more oven, um, entrepreneurial bent, which is something that I'm first doing now as well. And as in college, it occurred to me that there was this potential to technology that I experience from taking computer science classes. But I didn't feel that there was an opportunity to relate this to other students in the university with other very important skill sets, whether they be business students or or medical students of law school students. It's even very style owed within engineering, which you felt like a shame because probably because I didn't feel so strongly Self identified as an engineer. I like a statistician. I saw the value that and I I wanted people with other skill sets to also see if they could leverage technology in some way. So I organized. I started a student group at Columbia called Column Packed, and I organized a some type of hackathon in which, um, diverse types of participants could participate, whether they be in tears or business students or lost students at that organization together. I think for me built a lot of connections and experiences. I ended up bringing in IBM as a sponsor, actually on student Group, and that built those relationships that made it easier for me to then get a job there when I wanted Teoh. So that's kind of how I ended up as a data scientist at IBM.

So I say that my job entails, um, looking at business problems or stakeholder problems or customer customer needs And looking at what data sources we have available or what data sources we should look for that might have the answers or the, um, the fuel to solve these problems and then working together with my team whether it be a product manager in the designer and engineers Teoh come up with a solution where they're problems that might leverage this, um, data that could help them with whatever problem that they have. Working with a lot of different, uh, skill sets engineers and designers trying to understand problems and come up with solutions for those using, um, re sources that are mostly data resource is, um and I do that from at IBM. I worked 9 to 5, uh, with the no travel Julian. Um, but we've worked in the office every day above sea before Corona virus. Now, we'd never work in the office, but I can't the office every day. I would Job doesn't experience like I was enjoyable to collaborate with my team in person and brainstorm and come up with ideas

in data signs. The most popular tool or language I use is python because a very powerful language and it's a very simple language, and on top of it, you can very easily leverage a lot of different data science libraries. Other languages, Whether it's something like Pytorch, uh, there is something like psych. It learn very easy integrations between those tools and languages and python. If you're doing something that s'more, um, less machine learning focused and more data analysis focus, then you might use a language like our, um, but for the work that I did, which was more focused on machine learning solutions, uh, or customers that was all in python.