Yeah, absolutely. So when I was studying undergrad, I was a math major, so I always kind of had had maybe, ah, preference toward the more numerical work. Um, and around the time I was graduating, I I guess what was happening was the digital ad industry was really taking off these these kind of auction based add mechanisms like Google, AdWords and later Facebook ads. So my first couple jobs were in this realm either through like, more like marketing analytics, sort of advertising, ad campaign performance or also working on the ad operations themselves. Like figuring out the targeting strategies and and launching and monitoring these these ads. And I was doing that for a while, um, in New York and ultimately started at Facebook in maybe maybe like, eight years ago or something like that was doing similar work on and over time when I was working there, I I picked up sequel a little bit, and I was working in a lot of data visualization, uh, kind of systems as well, such as tableau. Just helping really large Facebook. Advertisers kind of figure out their their ad strategy. And yeah, I think what happened over time is. I got away from some of that more analytical work and it became more of ah, maybe maybe like relationship management with a lot of these large advertisers, and it was fun. I mean, I got to work with a lot of different companies, work with their marketing teams, data science teams, kind of just figuring out how toe best used Facebook advertising to their advantage. But I always, I think, kind of had wished that I went heavier into the more technical end, career wise and and just skill wise. And after some number of years, I decided I wanted to really give that a shot, and that's that's kind of what led me to these boot camps.

perfect. Yeah. So right now I work at O Reilly Media, which traditionally was kind of like a programming book publisher. But now they have this this online subscription learning platform that usually big companies sign up for for their employees. And then they can access thousands of text books and videos and things like that s o. I work on the product team as a data scientist for this online subscription, uh, product. And really, my job is kind of figuring out alright. How are people engaging with with this subscription platform and are their pain points? Are there areas where people are dropping off and then and then using it less? Or they're certain aspects of usage that really lead to them coming back mawr. And then how can we sort of highlight those those areas to get more more people to do it? So it's it's really just kind of analyzing. E. Guess you would say behavior in a sense of how people use this site and, yeah, and for the other part of the question with the priorities and pain points, um, I I'd say priority wise. It's there's certain metrics that we really care about as as a company. Um, some related thio revenue, some related to engagement, Um, and just kind of optimizing the work toward making sure we're moving those metrics is probably the biggest priorities. Pain points. I'd say it, you know, the It's growing quite fast. So the usage of the platform subscriptions great, like it's a is a growing business, but as a result of that, I think some of the data itself is very Messi. Um, you know, new features they're launching all the time. And, ah, lot of the work and probably the hardest part is just kind of managing, uh, what would sometimes very, very raw data and getting it into a place where you can you can get some insights from it.Yeah. I mean, to be honest, having a strong team of other close date of people. I don't think I could do it alone. There's there's three or four of us that kind of talking all all day long, working on the same types of problems. And without that kind of small network, um, I think would be very difficult. That's that's probably the biggest strategy and then the other. The other big one, I'd say, is just not making too many assumptions about the data you're seeing, like understanding that. All right, just because on the first pass it looks a certain way that might not be the case like you need to kind of more rigorously test out your hypotheses and make sure that what you think is happening is what's really happening before you try to start kind of like more deeply analyzing things and get insights out of it.

Yeah. So for me in particular, uh, it's been all really Python and and sequel, Um, at O Reilly in particular, I think we lean heavier on the the python side of things we like to build. Ah, lot of automation ourselves. Like, if we there's certain types of reporting that we find we're getting asked to do on a regular basis, then, yeah, we I mean, we don't want to do that by hand every time we want to find a way to sort of easily recreate that, um s so that's that's very somewhat easily achieved by linking python and sequel together. Um, and that's that's kind of the backbone for what we do. Uh, psychic learn for any kind of classifications or regression modeling tends to be what everybody on the team knows. So that's that's kind of where we built a lot of our our systems that way. Um, frameworks honestly, like, ah, lot of what we've built, we have our own libraries for first stuff. So we we just like toe have a shared kind of repository between the four of us that that maybe can use each other's code. Um, for for whatever you want to do. So a lot of a lot of it is custom by hand. Python