I'm currently working as a very engineer that track of so for my story, I think I have always tried to be, like, experiment like an entrepreneurial and just tried to something new all the time. So while I was pursuing my undergrad program in mechanical engineering again require experiences in robotics, marketing and cording and technology. So, out of all loudly, 11 master which I gravitated towards was starting my own business. So I was like, Where the curious to several ideas and topping so I can start My kitty is by heading myself as a suicide me die for my startup. So that was my primary, like the first carry that I ever heard. So right from then, I was trying to x getting toe technology and marketing understand the intersection better too. Sell my customers. So that's where I understood, like the power off data. What can what I can do with that? So that pushed me toe, gain more knowledge and fill those gaps in knowledge and gain a little more systematic understanding off how data can influence a business decision stats. That's when I decided to post my masters and uh, information systems at the University of Utah on DailyKos CLO business. So, yeah, that's that's kind of where I started and I moved to us both You Masters three months later, right from the start of my master's, I I was very actively trying to get into internship programs. So because I was never experienced with real time data science problem before, so I was trying really hard to get into. That kind of rule has been done. So that's when I started interning with Overstock. So that gave me like a really had a good understanding off how data can solve reliable, machine readable marketing problems. So that's That's my story of how I got in greater science. And from there it has bean like, more like a little, a little more three floor. Then how I had before I had to push myself through a lot more lot more unknowns before starting internship. So now it was a little more free flow, so I cannot, uh, got a full time job. But those star worked there for 1.5 years and then moved back and re educate a saint is so That's when I saw a different problem that I got interested in, which is mission learning Engineering. There is data science that has That's pretty interesting to me. But what I released Waas machine learning is really if effective, as ah, as with the scale like it, the scale is more data with a huge amount of data. That is, like, be amazing stuff that they can do with the mission learning. So that's when I can be with words and millions needing. So there is a good mix off data science and engineering in my current role. So this is, uh, this is really a to this point of, like, really enjoying ah, and working towards me, making myself like a better Emelin sneering then what? I was Estili. So yeah, that's that's their control.

primary responsibilities to, like build large scale machine learning systems for business to you to make decisions. Better are automated decision making process that slicker one. Line somebody off. But I want my responsibilities and primarily the decisions that I make kind of to park when I was data science site on the one news intern eating So they're the same side would be to basically decide how to which more to use are going to get the data from Understand the data with whom to ask questions at this is the decision that I am to make about the science part of the mission. Engineering Partners questions. I need to answer this in any to make our like, how to increase the moral production to put and how to manage memory efficiently, because when it comes to large scale, it has the impact on the in like the price that with the amount that we spent to get the computing efforts and computing the environment, is gonna increase exponentially if you don't man, is it properly so that's that's a really am back decisions that I would be making it with me today and work otherwise. I work 40 hours a week by my role. Simple. I'm job, so it's pretty flexible. Uh, sometimes I work a little more, sometimes less so. But yeah, for the arrows for two hours a week is what I work and travel. There's like pretty much no travel for me, except for the fact that we need a couple of times where our demon spread throughout the world like several locations. So we usually meet like once a year or twice a year just to know each other better. So that's yeah, that's but that's about it. About many cancer travel in my work.

in my current roles, primarily python as a airport where this court basis, like completely python based, Sometimes we use go for service building and by spark if you want. Oh, compute in a distributed system and beyond. So that's about like languages. And when it comes to modeling, it has always bean experimental like some we can try. Several models were can give. We were actively using X abuse and fire Stanley planning models that current systems. So, yeah, we, uh uh, if that's pretty, that's pretty, you know, not till consistent. We containing that. Quite often we have DT posts impossible to. We keep shipping models across ourselves and keep changing depending point at me. And and other than that, infrastructure is primarily bees Didn't abuse you. Stage maker. For our computing to stand even easy to instances for our stories, requirements use, we use esti Ah, In the past, we also used Google Stack um, seem purposes. So yeah, this this is like the primary set and there's like a little more tools for making are working easier like orchestration told Well, we use airflow. See a CD Jenkins on for ideas we used by term and uh, Jupiter, Not boots. So yeah, it's and being kindly experimenting with the, um, model tracking systems like Emily Flaw and group flow. The being an engineer, I had to, like, keep my hands on, like, several new tools. Understand those two, Inspector. So these are the kind of tools and this keep changing, but visa, like the So far, we have been using these a lot.