sure, I've had a pretty diverse backgrounds. So when I did my undergrad degree, I majored economics, and after I finished college was pretty interested in politics. So I worked for a lobbying firm for a year, and then after that, I moved to work for the federal government and the legislative branch. I did that for about three years, and then while I was working there, I got my master's degree in applied economics. My concentration was on financial, economic, so after that, I was kinda interesting. Million of finance. After graduating from grad school, I worked at a hedge fund for a little bit, and then I moved into working into aviation supply chain. And after that, I have been working for, uh, um, delivery startup. So I've been doing like, data analysis for that. Um so that's kind of how I got to where I am today in terms of like my career background before, um, the boot camp I did. That's privileged. 10 things I resented. Like last year I started boot camp with springboard for data science, just kind of want to get a little more exposure of things on a little bit more because I really had the skills gap, so Yeah. So the boot camp is pretty good. Decision enjoyed. It definitely made me kind of definitely solidified. Like a lot of the kind of skills I was learning, but kind of lagged a little bit, uh, made me a lot stronger. Coder programmer. Kind of give me a little more extensive knowledge about things like like unsupervised learning.

Yeah. So, uh, pretty much anything that's related to data analysis or kind of predictive analytics or forecasting is all under my domains. A lot of ad hoc analysis, things like, You know, it's like forecasting revenue customer turn during the customer segmentation, marketing optimization, um, working on some a B testing as well. So anything that involves analyzing data, the one that usually handles that.sure. So top three priorities are basically just like fine used data to find ways that increase company revenue. Theun Second was probably finding ways to keep customers on our platform, keep them engaged, and then last one is basically kind of helping make sure that, like our data, looks good. That data quality is fine. So pain points are really kind of the last ones they're like sometimes you just have, like, data integrity issues, data quality issues like you're kind of missing data points, and sometimes you just don't have a lot of data on customers and, uh, strategies effective dealing with that. That's, um, terms like getting more customer data. Usually, um, comes down to like surveys, so you can usually offer customers like a survey asked them. You know, certain things give them like a little promotion. They'll usually find that's usually a way to get them toe, reveal more information about themselves, kind of what's going what they like about your product, what they don't. So that's one way of dealing with it. Another way is just kind of kind of a B testing different layouts and sign up pages that way, like the data that you're missing, you automatically get when people sign up, so there's other things we're working on. Another pain point is really just kind of optimizing our marketing. So, um, there's just like social media in the main avenues we use and just doing with that challenges like, how do we actually know if people saw what we advertised? You know, our people actually engaging with a marketing, you know, our people, you know, is someone clicking on, Say something on Instagram is like Does it actually leads them using our products? So doing that, it's more just like analyzing the actual marketing data on that. So it's it's pretty challenging just because there's a lot of competition in our field, so it's just when it comes to dealing with, that's just making sure that your product is better and then just finding a way to hopefully get some sort of comparative advantage

um, e kind of mentioned this one. Talked about the boot camp. I'm primarily in the python like Anaconda day science stack. So primarily using python and then, you know, like you're numb pie pandas. Um, Matt plot lib for visualization And then, like when it comes toe modeling, usually using, uh, um, psychic learned and in terms like algorithms amusing, um, depends on the problem. Obviously, if it's a classification problem, will probably use something like K nearest neighbors or, like random forest. Maybe the ingredient boosting regression are, if it's sorry on the flip soffits or Russian problem will probably like random forests or Grady and boosting just because there fairly robust, pretty powerful generalizing. Well, um, that seems just from, like other data scientists people I know that's pretty standard across industries like Seems like Random Force are pretty popular. Um, yeah, I haven't really had much use for unsupervised learning yet. We just don't have that much data and also just kind of eats up too much kind of computation power. So very much sticking with things just kind of pretty standard, you know, logistic progression in Canaan. Um, linear regression, Random force