Internship experience becomes an opportunity after graduation for one student

Internship experience becomes an opportunity after graduation for one student

(Below is the transcript of a recent podcast between Dr. Keith Gamble and student Robert Mepham regarding real-world experiences in Data Science or you can listen here

(Professor Gamble) Before we talk about your summer internship in data science at Homesite Insurance in Boston, tell us more about yourself.

(Robert) Hi, my name's Robert Mepham, currently a junior at MTSU. I expect to be graduating in December of 2022.

So I only have one more semester after this current one, double majoring in business administration and data science.

(Professor Gamble) Excellent. Why did you choose MTSU?

(Robert) I'm actually from Chicago, Illinois, and my parents, they were getting ready to retire. They knew, right around when I was graduating high school, that they wanted to leave the state of Illinois. They picked Tennessee, specifically Middle Tennessee, and I wanted to be far enough away from them to where, you know, I'd be living on my own, but still close enough where I could go back on weekends and stuff. They're over in Cookeville where they got a nice house and everything now. MTSU was roughly an hour and a half away from there. So it's close enough to them, but also far enough to give me some distance. And yeah, I just fell in love with the school.

When I first came to Murfreesboro, when I think I was like 10 or 11 years old, the school was totally different back then. And then, eight years later, when I came for a campus tour, I thought I was just sort of doing my due diligence. I came here, and I was totally blown away with how much it had changed from what I remembered back then. There's been a ton of new building.

(Professor Gamble) Right. The data science major. It's still new, so what did you major in when you started at MTSU?

(Robert) When I came to MTSU, I was originally an information systems major that I chose that because I've always been into computers, into technology. I've always been into data. But at the time, they didn't have data science as a major. And so information systems, and it is sort of where I felt I could apply my skills the best going towards a potential career.

(Professor Gamble) Excellent. What brought you to the data science major?

(Robert) Oh, honestly, I don't know if there was one thing that particularly brought me to the major. I knew ever since back in high school, I've always been pretty good at math, and I started getting into sports analytics. And when I learned that, of course, people do that for a living, I asked some questions of the people I knew. I shot off a bunch of emails to people who I knew ran, you know, sports websites and stuff. So like, Hey, how did you get into this? And there were like 50 different answers, and no one had the right thing. And I asked them if you could go back and do it again, what would you major in in college that would prepare you for what you do now? 99 percent of them said data science, even though they had gone to college back in like the 80s and data science wasn’t the thing back then, or it wasn't called what it is now.

But if they could do it over again, they would choose data science. I knew that's where I wanted to be with somewhere in sports analytics, and therefore I think it kind of just jumped to me and I've loved it ever since I've started.

(Professor Gamble) Awesome. So let's talk more now about your internships. That was homesite insurance. What is does that company do?

(Robert) It's actually a very large insurance company out of Boston. So I like to say that the dirty little secret of the insurance industry is that a lot of the insurance companies you see advertising on TV, they're not so much insurance companies. They're more just marketing firms.

And so without naming specific ones, I mean, you know, the type of insurance commercials you see on TV,they will give their website or their phone numbers and their agents in their commercials. They'll take the policy, but they don't actually underwrite and service the policy. They pass it off for a commission fee to multiple companies, one of them being homesite insurance. And so, yeah, homesite really just underwrites. I think they have over three billion in written premium as of this year. So that's, you know, it's a fairly decent sized insurance company,and they do underwriting work for at least 15 or 16 different advertising type insurance companies.

(Professor Gamble) What was your role?

(Robert) So I worked in commercial operation strategy and analytics department, so that's for the commercial division of the company. So you've got homeowner's insurance and everything. This was for commercial insurance. So businesses getting insured, whether it be for workers comp like inventory, property insurance, commercial auto insurance, that division of the company and then strategy and analytics. So it's actually it was a new department at the company.

When I started, my manager, Alex actually had just gotten the promotion and was told to start this new department because they know data science is an ever-expanding field, and they knew that the way that their company was operating. It was staged to grow.

It wasn't really going to be an inefficient way. They knew that they were going to be wasting a lot of man hours doing certain processes and excel, you know, by hand and on paper that they could do, given some software engineers and some data scientists.

And so our goal was pretty much every week we would go and consult different groups around the company, different departments and different teams. And we would say, what are you guys wasting the most time on on a weekly basis? And then we would monitor them for a couple of weeks, see how they did it.

Usually it consisted of, well, we'll write a SQL query to pull this data from our database. We'll go ahead and put that in excel, and we'll do this list of 100 things to it. And then we send out this file to all, to a vendor, to our manager or whatever. And of course, as you know, in data science, if you have a strict enough set of rules and stuff and what your end product is, you can go ahead and get that down to a 30 second script. And so that was basically my job. The entire past summer and fall working for Homesite was developing those sort of solutions for the company.

(Professor Gamble) You mentioned SQL as a tool. What other tools did you learn in your coursework that you applied?


(Robert) So as far as programming languages, one SQL was a big one, especially because that's their data management language. But Python and R were also two other ones that I used on pretty much a daily basis, and it was basically down to whatever the manager wanted of the specific team that we were consulting. Whatever the group lead wanted, if they wanted a solution written in, or we would have to figure out how to write it in or if they wanted it written in Python and like automated through IWC, we would have to write it in Python and work on getting like an IWC repository hosted for it.

So yeah, just learning, learning how to code and not so much learning the specifics of like how to write this sort of algorithm or this sort of structure, but also just understanding. How to translate a set of like work requirements or project requirements. And no, here's generally what I'm going to have to do as far as coding wise and then go on Google it, you know, go out and say, you know, doing such and such in Python and then be able to scroll through Stack Overflow or something to find something close to what you need. Go and manipulate it a little bit for your specific deployment.

(Professor Gamble) What at the end of your internship were you most interested in learning more about? What did you feel like you needed more education in to help you in the next role?

(Robert) I guess what I wanted to learn at the end of my internship, like you said, would be deploying models.I know in classes and on the job you learn how to, you know, manipulate, like, extract, transform, load the data, train any number of models, you know, predicting whether it's classifying or regression to predict some sort of target variable. We learn all that. And then it's, OK, that's great to do it for the training dataset or the one question you had. But in the business context, usually it's a situation where they want that report run every single week or every month or every quarter, or they want to be able to as part of the insurance thing, the quoting system. So when someone goes in to get an insurance quote, they have to be able to take in all this data on them and they have to be able to provide them with a quote, like a monthly premium quote. And that's based on a whole whole bunch of different risk factors and risk tables and stuff and a bunch of other variables that we're able to look up on them based on whatever data they give us. And so being able to develop a model that takes in continuous data from streams like that and to be able to give continuous output, that was sort of where I felt I didn't feel unprepared. I felt curious if, well, I wanted to learn more because I knew how to do all the training of the model in the, you know, the reports on. Here's how effective my model is. Here's, you know, the AUC and the ROIC curve for my model, but now it's OK. That's great. Now let's get that working in the business context on an ongoing basis.

(Professor Gamble) What is next for you this summer and beyond?

(Robert) So I'm actually going back, I think, as of right now. So it's mid-March, I’m fairly certain I'm going back to homesite. They've asked me to come back. I still have a couple other job offers on the table for this summer, but it looks like the best one for me in terms of where I want to be for the summer and what I want to do is going to be going back to homesite, continuing with the same team I was working with last summer. And so that's for this summer and then beyond.

Like I said, I really want to work in sports analytics, you know, data science as I'm sure a lot of people who have taken any sort of data science class here at MTSU knows it can be applied in literally any sort of business, whether it's retail, insurance, academia or whatever it is, there's data science that can be applied anywhere. And so it kind of just depends on what you are passionate about as an individual.

And for me, that's sports, right. I can run insurance numbers all day, but I'm never going to be super passionate about, you know, look at how much written premium we're going to have in August of 2024. It doesn't get me going the way that, you know, certain sports topics do. And so I'm not really sure how I get my foot in the door in that industry, whether it would be to go get a master's in data science or sports management or something like that, or to hopefully maybe land a gig after graduation with some sort of professional sports team.

(Professor Gamble) Boston is the home of the insurance company. Yes, a major hub of sports and professional sports activity. How did you like living in Boston?

(Robert) So I actually did not live in Boston this past summer. It was a remote work. I was able to visit Boston for a week, which was really cool. Their office is right downtown across from the Garden where the Celtics and the Bruins play. So it was it was interesting to be down there for the week. I was put up in a hotel for the week by the company, got that nine to five experience going out on business lunches and everything with my manager and other people. So it was really cool to have that one week experience. And then for the rest of the summer, it was virtual, you know, distance and stuff. So I was working out of my bedroom, which, you know, have a desk in the corner of my bedroom. That's where I did most of my work out of and it was pretty cool.

(Professor Gamble) Thank you for interviewing with me.