sure. So I think to define where I've gotten to today we have Thio define where I am today. Eso I currently lead data science and operations for PhD Media North America. Eso That's a subsidiary of Omnicom Group, which is a very large holding group. About 80,000 employees. And we way handled everything from advertising pr thio just media distribution for, uh, whole gauntlet of Fortune 500 clients, footsie, 100 clients as well as, uh, some of the larger startups out there. Eso that's that is where I am and what I dio on as to how I got here, I started out with a masters and e con statistics econometrics from the University of ST Andrews in the UK That was kind of my, uh, inspiration in some ways for getting into data science. I really was taken with my econometrics class and immediately out of out of university. I started trading equity models actually, that I've been building in university s O. That led to the foundation of my first company, which is called one Howard Capital on Guy Operated that I think relatively successfully for a couple of years we beat the S and P 500 by a fairly substantial amount. Um, unfortunately, a couple years in, um, the inputs to our models all became outliers in my models. Didn't know what to do. So I ended up having to make a pivot in that pivot was into Medicis, uh, a data science boot camp. And it's sort of an intensive three month program there that was in San Francisco and that was really instrumental into the next step of my career, which was joining, and An Elect, which is the Data and Analytics subsidiary of On the Com Group S. O. I was immediately hired out of medicine to an Elect, and I've been with Omnicom Group since then.

sure. So we're lead data science and operations. Uh, for the most part, II ensure that our data science, engineering business, intelligent teams air delivering work to client specifications. We are very much a client services businesses. Excuse me. Client service business on. Do you know, satisfying client needs is our number one thing. Um, so that's that is probably goal number one for me. Uh, number two is just evangelizing. New methodologies produced by myself and by my colleagues Thio to our clients. A swell as to the industry, Eso you know, that's that's kind of ensuring that these work streams and, um the, you know, the work that we're doing really maps thio, business value as well. Azaz research value. Um, yeah. Uh, sure. So in terms of, you know, top top three priorities and pain points. Um, really. My number one priority, frankly, is ensuring that my my team, my colleagues, have a safe and comfortable environment. Thio work and experiment. And, um secondly, I would say that you know, it's ensuring our work is, uh, you know, producing consistent business value where that can often get lost in the shuffle. When, when you're working in data science and, you know, you're you're caught up in some very exciting research project or you know, you you you seeing some new white paper and you're very excited to implement it. But it may not necessarily have ah, use case within the context of your business. So it's important to keep focus on track there, I would say on dfo finally, really just, you know, ensuring that my team is advancing. Um, you know that that my colleagues, my direct reports, are moving to the places that they want and learning the things that they want to dio.Yeah, sure. So I think, you know, top three pain points. Um, really are kind of in at least my my current company and situation. We're dealing with a bit of a cash crunch. At the moment. It's the result of the pandemic. So re sourcing and staffing through that is absolutely been a challenge. Um, not to say that there's not plenty of talent out there. It's just more of a challenge from the finance perspective of our company has to pay out a certain dividend every quarter. And in order to meet that, we really we really actually operate on finer margins than one might imagine for a large company, eso that that that has definitely been, Ah, large pain point. I would say the next pain point is more applicable toe just sort of any any environment pandemic. Regardless, uh, that's balancing competing priorities with with our teams bandwidth, especially in a client facing client services business, you get a lot of requests coming in from a lot of different people with a lot of different perspectives on. Do you know some of them could be very technical. Some of them might be less technical and just sort of have desires and, you know, operate from the perspective that data science is, you know, some sort of magical tool that just makes things happen. And, you know, it's it's really, um, conveying to them and in a powerful way, what can and cannot be done and ensuring that, you know, uh, my team and my my colleagues or not are not overburdened by requests that, you know, really, really shouldn't be filled by a data science or data engineer. Business intelligence Onda. Finally, I think third paint point is just, you know, seeing securing that advancement for my team in the pandemic. Um, you know, as I mentioned, where we're working on fine margins and there is a cash crunch, securing advancement is difficult. Um, where, you know, you could make great cases to finance of Hey, we've we've, you know, done X, y and Z that really move the needle here for the business or for our clients here that generated this much revenue or whatever the thing is. But even so, you know, in a large company, finances are complex, and that doesn't always necessarily lead thio immediate advancement, especially in a pandemic environment. So securing advancement is certainly is certainly a bit of a pain point, but, you know, it's also something that gets worked through and certainly not normally a knish you outside of the outside of a pandemic environment.

Yeah, sure. So we use kind of a gauntlet of different software programs, frameworks, models, algorithms and all of that s o primarily within my world are statistical, uh, Mawr center data scientists. So the inferential statisticians, those guys will be focusing mawr on using our the our stack. Uh, those who are more focused on what we would call audience data science or marketplace data science or real time bidding algorithms. Thes thes types of things are generally done in python. Um, absolutely. Sequel comes into play when? When querying databases way. We do have a bit of no sequel in our environment, but Sequels sequel is very important. Um, but truly I think, you know, Python is one of the most important programming languages here where a recent developments and language of really, um uh really sped up the way that you can iterate and build on modeling and, you know, algorithms and all of that where you can you can easily put things on GP use on python. Now you could distribute things over Cooper Netease, clusters toe, you know, really, really dive in and create some great analyses out of big data. Um, yeah. So I think that's, you know, sort of sort of the gauntlet of tools, and I should mention as well we are. We're in aws that companies. So we are focused on Amazon's tools. Uh, not to say that we're diametrically opposed to, for example, you know, Microsoft Azure, Google's cloud platform. But we are in need of U. S companies so that that is where our focus has been.