That's a good question. Um, I mean, it's it's it's it's been a long journey. Um, so, uh, you know, there's a lot of schooling. Ah, a lot of, you know, practice. So I studied physics, physics, and my factually since I was, um, you know, in the early edge, um, secondary school university, grad school. But I had, like, various interests. No, just physics. You know, I like to read a lot. So I was causally, like, reading things from, like, different topics, you know, business, finance, history. Yeah, T you know, burning up my sculpt. You know, um, according ai, it's all those things, right? And, um, a combination of both my academic training and my interest is actually what led me to you know, where I am now. You know, it's not necessarily, uh, subscribing toe one direct path, you know, but more like exploring, like, different options and, you know, going where I think my interest lives.

right now as you well aware because of Corbyn 19. So everybody has been working from home, right? So it's mostly like I work from home, but usually before this situation, actually, before the look down I was working in in an office, right. So there was no travel there as well because I saw office job and, you know, so, like, the job off a data scientist at Adobe is also a little bit broad because it depends in what group you're affiliated with. So my group works with the product team, and our job is to use, you know, help the product team by adding some machine learning and artificial intelligence features to the products that we sell on the market. So, you know, day today, mostly it's ended the run like a productive trying to deliver. So they, you know, like you work with the product manager. They tell you were trying to add this feature. So and that's the start of a project. And, you know, then you spend you couple months in which you know you meet not only with the product, but you get to talk to some of the customers. Try to understand what the problem is. Um, then you talk to some of the engineering teams trying to understand You know where some of the technicalities lie and, um, create like a prototype, which more is usually like a proof of concept just to see if the desired features are enhancements and the products actually visible. And once that is obtained, then you go on to now building like a full scale solution that gets integrated. You know, we like all the existing products, right? And, um so that is the journey off, you know, work when you look at the whole work stream. Now, when you segment that on a day to day, it's a little bit buried like on some days you'll spend a big chunk of your time attending meetings, meeting with other people to try and you know, brainstem or interviews to try to get a perspective. On other days, you spend two days just doing cording. So which is implementation, right? And there's also preparing presentations, talking toe architectures on, you know, doing some you know, didn't work. So it's a little bit of a mix, but if you were to give it like a racial ratio, I'll probably say, Are you spending Let's say like 60% of your time interacting with others with other people and 40% of your time doing development work.

I I I see, like a job positions online all the time in people asking to know, like a lot of tools is very true. We use a lot of tools. You don't need to know all of them, right? So we use python like everybody. It's kind of well aware, you know, python soup, iPhone and Seiko. Very important you need. At least you need to know those, right? But then there's also JavaScript. Um, And then there are other tools that we use, like a floor new relic. Um, so in And another, like an other laundry list of tools that we use mostly, um, you know, depending on the ah, like on went where you are in the development phase, right? You know, but mostly for development purposes, it's Seiko and pipe.