JP Gallagher '19 & Amy Wikiera '21

JP Gallagher ’19 and Amy Wikiera ’21, both graduates of the Computational Modeling & Data Analytics program, joined the Data Science team at Blackstone, the world’s largest alternative asset manager, after leaving Virginia Tech. At the time of this interview, Wikiera was part of Blackstone’s Data Science Management Program, which involves one-year rotations at different portfolio companies in the Blackstone ecosystem; she now works full-time at one of those portfolio companies, LivCor. Meanwhile, after beginning in the Data Science Management Program, Gallagher works on the sourcing and alternative data team under Blackstone’s Data Science umbrella.
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What is your current role at Blackstone?
Amy: I'm in the second rotation in the Data Science Management Program. I’m fully deployed at LivCor, which is Blackstone’s multifamily real estate portfolio company. My main role is to work on impactful projects at LivCor. I'm working right now on their Data Insights team, finding areas where data can really have an impact at LivCor specifically. I’m also providing that communication channel between the Blackstone Data Science team and now the Data Insights team at LivCor. It has been really cool to be a part of both Blackstone and a portfolio company.

JP: I think broadly the approach here is to try and use data science at all points in the investment life cycle across all our funds. Whether that be traditional private equity or real estate – which Amy's portfolio company steers more towards – as well as other funds, like strategic partners. What that looks like broken up on our team, typically there's three main pillars of that process.

One would be like sourcing and top of funnel. We’re trying to use the alternative data sets to identify investment opportunities; that's kind of where I currently sit right now. Then there's more traditional deal diligence – once we receive first party data from a given portfolio company, we’ll look at that asset, understand kind of the performance and business leverage of a given company. Lastly, there's the value creation level: after we've made an investment into a portfolio company, how can we help improve data operations? I think Amy's work at those portfolio companies is the best example of how we're driving data science forward across the portfolio.

What did your journey to working at Blackstone look like?
Amy: Me and JP were in the same group when the Blackstone Data Science team came to Virginia Tech in person and interviewed people for the internship, as well as full-time positions. I was a junior at the time, and I was lucky enough to get an interview with them to be a summer intern. It was COVID summer, so it was a remote internship, which was tricky, but I still learned so much from that experience. At the end of the summer, they offered me a role in the rotational program after I graduated. I was like, “Oh yes, that sounds awesome!” That's kind of how I ended up at Blackstone. Now I’ve been here for almost three years, so I did the internship and transitioned to full-time after graduating.

JP: Yeah, similar to Amy, it was an on-campus hire. I was already at my Capstone portion of my CMDA career, and my Capstone project was actually with a Blackstone portfolio company. I made a large effort to get on to that team because I was definitely interested in this space. And then when the Blackstone team came down to campus, it was a great opportunity to meet the Data Science team and get to interview on campus, and then ultimately, I started full-time upon graduation.

How has your time at Blackstone shaped the way you view data science?
Amy: Coming out of college and entering Blackstone, that's when I really learned how data science is actually applied in the real world. I think the biggest thing I learned is half, if not more, of the problem is understanding the impact of what you're doing and communicating it. That was so much more part of the data science problem than I thought. I think Dr. Embree captured that in the Capstone class in CMDA and now looking back, I'm like, “Oh, that's why they put such an emphasis on – when you got your Capstone assignment, before you even start the data science project – to think about what's the impact of what you can do.” It took me coming out into the real world to realize how important that is and why they emphasized it. I'm like, “Ah, that's why they taught that.” I think that’s the biggest thing.

JP: Yeah, I would agree with everything there. I've always thought of data science as primarily a problem-solving framework rather than necessarily an independent area of study, so for me it was always about using data science to solve the business problems. Amy and I have worked with different portfolio companies as well at Blackstone, and it really shows how powerful and how efficient it can be as opposed to some of the manual operations. I thoroughly enjoyed learning it in school, but then really got a sense of how powerful it was once I got out and saw some of the more traditional ways people were going about solving problems. We are able to apply a unique lens to that and use data science to just make things much more efficient, and as Amy said, impactful.

What is it that you most enjoy about the work that you do?
Amy: I think it's really enjoyable and rewarding that there's an aspect of creativity with data science.  It’s like what JP was saying with problem solving. You come to a company, and they have problems and they might have a traditional solution to it, but being able to think about “How can I make this more efficient? What can be done here?” It does take creativity and there's not always one answer to the question. I find that really rewarding and exciting.

JP: Yeah, I’ve gotten to wear a lot of different hats during my time at Blackstone. I've really enjoyed getting to see early on in my career, a lot of different positions, different roles and different industries. I think that's probably been my favorite part so far, just getting to have a wide range of experience so early on.

How does the Data Science team at Blackstone function?
JP: Right now across the firm, there's roughly 50 data scientists, broken out by different asset classes. On Blackstone’s centralized data science effort, of which Amy and I sit, we have closer to about a 30-person team and people specialize in different verticals, which we've already touched on a bit.

As far as the way the team operates, within those verticals it’s very collaborative. Often we have general team calls, so even if you're not working on a problem related to somebody else's group, you still have a sense of what's going on. The reason being is that the application of data science, even though the problem might be totally different, the solution and the skills used to solve it, there's a lot of learning opportunity. It’s that type of collaboration that's really helped us all make each other better.

Amy: Within the rotational program, there's currently seven people deployed at different portfolio companies, which also creates a lot of interesting conversations because we're in different industries. I'm within real estate, there's people in the entertainment side, someone’s at Ancestry – just really cool different industries. There’s seven of us that are deployed out and are able to connect back with the core team as well, which kind of makes for an interesting dynamic.

How did the CMDA program prepare you to work in these roles?
Amy: I think the Capstone class was huge. It was really helpful to go through a full case study, a Capstone project, from the beginning to end before you graduate and go out and are doing similar structure projects at companies. That I think was really huge in preparing you for how to scope a project, communicate a project, all of it. So, shout out to that class.

JD: Yeah, I have the exact same answer. I think Capstone's great in preparing you for the type of role Amy and I are in. First you have to meet with your sponsors and understand what the problem is, what the business is, what they're trying to achieve, understand the expectations that they have for you. That’s a lot of what Amy and I do all day. And then additionally, what I think the course does so well is it supplements the data science efforts with written updates and presentations throughout the course, as well as deadlines with your sponsor. I think that really is helpful in preparing you for working hands-on with different portfolio companies or business leaders across the firm.

Did you have any favorite classes outside of the Capstone in CMDA?
Amy: I loved the math modeling classes. I thought they were awesome. It was also the class that I was working with Python in. I think at the time that I was going to the program, it was probably more heavily R based, and that's when Python was sprinkled in. I think maybe it's transitioned now to be more Python, I'm not sure, but I really loved those classes, and specifically linear algebra. I find that super interesting. I really love those types of classes.

JP: I liked everything with statistics. I picked up the stat minor when going through CMDA, so that was great. I enjoyed all those classes, especially culminating in the machine learning course. Also, I liked linear algebra a lot too. I liked when everything tied up neatly in the matrices, so that was always a fun feeling when you're taking an exam.

What advice do you have for current CMDA majors?
Amy: What I wish I would have thought about more when going through CMDA was what industries interested me. Because you can get really into data science and learning all the tools and techniques, but you're going to apply that somewhere, so you should really figure out what you're interested in. I know that there's now like a sports analytics club and those kind of things, and the Pathways is really guiding that, which I think is awesome. But if you come near the end, and you're about to graduate, and you don't know what industry you're interested in, I'd say Blackstone is actually a great place to think about because you get to experience so many different portfolio companies being on the Blackstone Data Science team. I've appreciated having that opportunity to explore industries and what I was interested in post-graduation. But I wish I thought about it more during [school].

JP: The advice I would give is nearly the exact same. I think the skills you learn in the CMDA major are applicable everywhere and are sought after by a lot of businesses. So just make sure you're working in a field that's of high interest to you and you can continue to learn. I think you are well set up to do that with the skills you pick up in CMDA.

Amy: I'll also just add: remember to try to take fun classes outside of CMDA, because a lot of different classes also benefit your skills becoming a data science professional. I took an intro to acting class and that public speaking aspect is huge for presenting and pitching ideas to stakeholders. Remember to think about other classes that can help balance you out, because being a data science professional, you need so many different skill sets.

Anything else that you think people should know?
JP: That I miss Blacksburg and can't wait to go back.

Amy: Yeah, enjoy your time at Virginia Tech. I'll say CMDA is truly awesome. Looking back at the professors that I had, the opportunities that different professors gave to me, it's just huge. I miss CMDA. I miss those classes. It really is a great experience.