Happy to share my story. It was a different path. So at the David Eccles school business, I did a major in both finance and information systems. And so when I first went to the U, I was pretty sure that I wanted to go into investment banking. So I joined the student investment club and then there was actually a professor at the U who for my undergrad, I took an IS class. I've always kind of liked computers, I've liked technology analysis. And so that really started to pique my interest. So then that's when I chose to do a major in information systems. But right out of college, I first went to Goldman Sachs, and I joined their team. I spent about four and a half years there. At Goldman, they kind of put people in the role, and you have no idea what department specifically will be in, but you try and kind of find your place based upon your skillset. And so I quickly found myself in a skillset where I started with very basic, started developing access to the databases because they wouldn't allow us access to production on the technology side. So developed these access databases and actually groom into programs that the company could use to become a lot more efficient on. So this was in their assets servicing division. Fast forward, two years later, they started developing this thing called Strats, which later became known as data science. We're the cornerstone of how analytics from an operational perspective, took the light of Goldman Sachs. I spent about four and a half years there and then quickly realized I wanted to progress my career more on hardcore analytics and data science, enjoying the company where I could really move the needle. So then I found myself at this company called Crest Financial and I got really heavy into data science. And, of course, when I was at the U. Data science wasn't a term that was used, and this is back in 2011 when I graduated. Fast forward a few years that start to become like this term that's being thrown around a lot. Especially as companies start to gather more data. They need to do something with it. And so I became the data scientist at this company Crest Financial. And I was in charge of developing and underwriting the crust, credit risk underwriting model. I had never done data science before. I had never done hardcore statistics and so I did a lot of online certifications but used my foundational knowledge that I learned at U in terms of database management, thinking about relationships, thinking about how to use some of my strategic management courses. Today the kind of call a pure data scientist, a unicorn. So it's somebody that has great knowledge in terms of subject matter expertise. You know a lot about statistics, and you also have a good understanding of being able to write code. Call it python, R or SQL. So I kind of found myself in that position where I had a very good grasp on all three of those. There was another person of the company that joined that was purely academic, that a PhD in statistics. But they had not spent a lot of time on the business side and they weren't able to connect the dots and really kind of march forward developing this algorithm they were really supposed to do what the company wanted to do. And so I then found myself in this position. I was like, "I have a skill set of where I can really add value." So I spent a couple of years of dark company and then said, Okay, I really want to drive like the full analytics architecture for a company and I wanna be able to move the needle because that's kind of been my career path. I want to build things and really change the evolution of a company to succeed. So, then I moved on to a company called Womply. It was headquartered out in San Francisco. I'm currently there, director of Analytics and Data Science. And when I first joined the company, I just joined as an analytics manager. They had done about 50 million in fundraising and their main goal was to service small to medium-sized businesses. So this is a very different space, right? I went from banking to Sass, which is a completely different type of business and so that was a little bit challenging with itself. But I quickly found myself with my skill so that I found in my other company to be able to shape their foundation, and I came in and I said, "You don't have the right talent. You don't have the right structure. I've been here and seen this before at other companies. This is how we should be thinking about solving these problems." I built the team-up. I got promoted pretty quickly to senior manager and then pretty quickly after that, got promoted to director of both analytics and data science. That's kind of the story of how it got there. Obviously a different path. One between banking and sass. And then, What incidents are experiences shaped my career path? I think I kind of talked through a couple of those in terms of just seen a clear need for something where there's not a person or a team being able to fill that need. I think it was about me raising my hand to say, "you need this" and not being afraid to push back with my opinion That has always been something throughout my career. That's it's never comfortable right to dispute what kind of the status quo is and to be disruptive and disruptive in a way that builds the company and propels it forward in the right direction, not disruptive from the aspect of causing chaos. And so I always had a very firm footing on where I stood and why I thought we needed to do certain things and was backed with research. It was backed with what we were going to do from a company perspective and how this would benefit the company and propelled forward. And so I think through some of those things it just started to iterate and that, once I started to see that work, and it actually did help the company, and there was enough trust builds up that's ultimately like how there became a pretty quick progression to where I am today.

Great question. So I'm gonna start with the second part of the question and then we'll move into the responsibilities. Womply has headquarters in San Francisco, and then they also have an office in Lehi. Our office in Lehi is the largest one. A lot of our sales team, Customer support success, finance, analytics, corporate marketing, HR, people ops, those teams sit in Lehi office. And then, minutely, our engineers sit in the same office. So there's a lot of cross work collaboration between the offices. So a lot of what we do is via just Google hangouts with conference room, set up cameras and video monitors. So that's a good way to have crossed collaboration. With that said I typically at my level, and even when I was senior manager, typically travel out to San Francisco about once every couple of months just to check in with the team. I follow up on projects. I find that it's a lot easier to a whiteboard and get like big projects off the ground when you're kind of there in person. In terms of running the business, moving small things along. You can kind of do that remotely. It is challenging, but it can be done. In terms of working from home, I definitely take full advantage of that. Even though I am at the level I am, I do still typically spend probably 2 to 3 mornings from home and every other week at least a full day from home and so our company culture is kind of about getting to results. It's not about face time. It's not about how hard you work, how long you work, it's about getting to results. And so our team, funny enough, is really the team that kind of built that trust for our company culture to allow people that flexibility. When I first joined, it was completely taboo to work from home, but I definitely pushed the envelope because, in my prior company, I worked from home a lot because I was definitely down in the weeds to bust out of the project I needed no distractions. But with that said, there is kind of a fine line to play because when you do become a manager, you need to make sure that you are there for people to unblock them. You're there to make sure that they feel supported. And you're there to lead by example. And when you do have people in the office, you do have to do that. So a typical work week for me is probably about to call it 80 hours. That's not by demand. That is by choice. And that's about being empowered. A startup is about really moving the needle forward. And it's hard work to wear multiple hats. And so a typical day for me, it looks like I wake up at eight. I hop on my computer. I'm working until about noon. Quick lunch. Drive into the office. Stick there until about 6:30 Come home. I log back on my computer around 9:30 and sometimes I'm working. It's all 12 sometimes till two. Interestingly enough, none of this feels like a job, which is obviously a really cool place to be. There are definitely days where you're like, "Oh, man, this is you know, it could be rough, right?" There are gonna be disagreements there could be projects that are stalling that are hard to get through. But at the end of the day, the things that I do work on, I find very intriguing, very interesting. And I like seeing things push forward. So that is definitely a good thing. That does not always happen at every company and in every role. But when you do feel when you do find the thing that you're most passionate about, I can definitely test it does not feel like work. In terms of my responsibilities at the company right now and the type of things that I handle. So I manage both our analytics arm and our data science arm and our companies had interesting points where we're starting to expand very heavily into the data space on. And so, from an analytics perspective, it's really full scale. We are a central analytics team. and so we have an analytics manager and then we have analysts that report to him. Their main responsibilities are for basically being they have BI developers, so they will actually develop the data models on top of our data warehouse to report on the business that will develop visualizations through demo or Tableau that could be used throughout the company. They will then run occasional statistical analysis tests where we help, like, call it our marketing team, do an AP test on a particular like maybe a direct mailer. Or maybe it's some type of advertisement that we're gonna put on to Facebook or Google. We'll help them with those types of tests. Additionally, we'll look it like, How is our product working us? We'll do various tests to see if our product actually delivering on the value that we are promising our customers. Luckily for us, we have hundreds of thousands of merchants that we work with. They were able to kind of have a control group by default and then our test is really our customers, which is kind of a nice place to be. That's kind of the scope of the analytics team. With that said, because we are a startup, they do wear multiple hats on that side. So we do things. They're more along the lines of, like, data operations, so call it will build some of the pipelines that exist to call it, somebody comes onto your website, you want to create a lead, you want your sales team to call them. We build a pipeline to capture that data, put it into a CRM tool and then work the mechanics to make sure that somebody is actually going to call them. Another aspect is we use a tool called Segments, which is essentially a data bridge. Within that, we own the entire architectures, that's capturing all the metadata about people that land on your website and then how they are actually then transitioned to using your products so you can understand, like feature importance of product usage, the stickiness factor of like what things drive the user back into your product. So would use some of that Web and Product Analytics as well. Then on the data science side, that's kind of, new fund space that we're starting to build-outs, what we're doing there is on one of the responsibilities are essentially developing these new ML models that will do fuzzy matching. Within our product, we have a lot of credit card readers, a lot of merchant data, so we're building consumer profiles, business profiles, understanding consumer behavior spend across various merchants, and so there's a lot of analysis that goes into that. There's also a good chunk of ML that goes into matching, call it hundreds of thousands to millions of businesses or transactions, and you can't have somebody sitting there going like, "Yep, I'm gonna do this quick match, this address generally looks like this looks good, right?" You can start out that way, but in order to scale it, you have to have a model that starts to be able to recognize that pattern. And so that's been mainly with that team's gonna be focused on. In terms of, like, general responsibilities the typical type of things. Weakly one on ones with all my direct reports. I handle all of our team meetings across my organization. I work cross collaboratively with other leaders of the company, mainly at the VC level, about how can analytics help them either answer questions or push the business forward into new endeavors where we've identified an opportunity, Or how can we increase our overall revenue? How can we become more efficient which therefore increases our revenue by reducing our costs? I handle all of our budgeting and forecasting for our team.

I was a Microsoft SQL server guy for a really long time. So I knew t-SQL really well. It wasn't until I joined Womply that they started using a tool called Snowflake, which is essentially a cloud data warehouse. It's built on AWS. Snowflake has blown up over the past couple of years, and by far, that is my favorite tool to use. In fact, I've spoken for them at a couple of their events in Salt Lake and I'm definitely a power user of theirs. The nice thing about it is, you're able to run a query and you can dedicate these warehouses or essentially cluster compute cores to process on your query so you could have called it like an ETL warehouse where you're putting all of your ETL pipelines and work through that. Then you can have something that like your analytics team is using. You're able to spin up another warehouse that has its own compute cores to run the jobs. Neither of these compete with each other. So when you talk about like bandwidth, you start to reduce it that way. Additionally, on the fly, you can scale those warehouses up and down, based upon how much work you wanna put through them. So call like you have a query that, you're looking to go through like trillions of rows of data. And it's a very complex query. You can just easily like, click and drag, scale it up to, like, 24 compute cores. Your results will be back super quickly. You don't have to change any hardware where it is like On-Prem, forget about it. That's not a possibility. That's one of the reasons they have blown up. But the entirety of our analytics architecture is built on Snowflake. So that's when it comes to data in storage and retrieval. One of the best tools. When it comes to machine learning, I've played with a couple of tools that are more driven, some more driven towards like data science heavy users, some towards like business analysts? One of the tools that have used historically is called Data Robot, and it was very quick to use in terms of like, I don't want the best model out there, but I also don't want to spend a lot of time like developing this. They also had an API which in my prior company was super useful, right? You didn't have to write any code other than giving a JSON payload to this API, you get the probability returned back. So we talk about, like, credit underwriting. super-valuable, very low cost that does not prove to be super useful, depending upon your level of accuracy and how clean your data is. So with any type of auto ML platform? It's like garbage in garbage out. Almost 95% of the work you do is about cleaning up that data set and making sure that, you've removed a lot of the bias in your data. There's likely always going to be some bias, but at the point, we're at Womply, to be honest, we use right now is a combination of both Python and R and just running through the standard algorithms that exist because a lot of our time was spent on that pre-processing. We don't need anything fancy, or quick through an API front. So, R is definitely my preferred tool personally, that's also used on our team a lot because a lot of what we do is statistics-driven, all the packages in R are geared towards statistics. When it comes to, how efficiently does a script run? R is really not that great, Python is a heck of a lot better if you're trying to run something quickly. With that said, there are a lot more packages to install. It can be a little bit more complex to get to, but we definitely do use python for a lot of our ETL processes in terms of like getting data out of our core production databases, which are already pushing it into Snowflake. That's all driven through Python. A lot of our data manipulations that we can't do in Snowflake through a typical SQL Query, we'll then use Python, push that into an EMR Instance in AWS using spark and then pull the results back through Python. So there's like, definitely necessary things. But I'd say if I could pick through tools that are the cornerstone of what we do, it's R, Snowflake, and Python.