Description of Current Work

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Q: What got you interested in financial engineering specifically, and why, do you find this field exciting?

 

A: ​So, what first got me interested was… I don’t even really know how—it might have just been from seeing some random posts on LinkedIn. At the time, I was working in real estate finance, and I found the field to be a bit antiquated. I liked coding, and I really loved math—I had studied it in school—but I wasn’t really able to apply it as much as I had hoped.

When I was in real estate finance, I was mostly using simple, arithmetic-type math, but I wanted something more advanced.

So I started looking around for something more heavy-duty, if that makes sense. That’s when I came across financial engineering—quant finance—which combines calculus, statistics, probability, and coding all together. That’s what really drew me in.

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Internships and Breaking into Quant Finance

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Q: Can you walk through your internships and early experience, and what you worked on in each role?

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A: Yeah, sure. So, there are, I guess, a bunch of different internships. I won’t talk about the one in undergrad because that’s not, I guess, too relevant, but during graduate school, instead of actually doing an internship, I… well, I’ll say I took a class with a professor over the summer, and a lot of it was basically just independent AI research for companies that came in and asked students to do projects with them.

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But I had taken it for the second time—like, they were slightly different classes—so it’s not like I failed the first one or anything like that. So the professor knew me, and he basically just said, you know, instead of doing a project, do an internship with my little startup that I’m running. So I did that, and it was really just helping them optimize some of their data, because they weren’t really using some of the Python libraries properly.

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And then I’d say the second one, right after graduate school, was for a very small hedge fund called Dropshot Capital. I actually wasn’t able to find a position in financial engineering, so I just reached out to them on LinkedIn and asked if I could do some free work for them. So they let me have a position there.

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I was able to basically remote into their server, because they had a GPU that I could use, and a lot of it was doing things like taking a lot of ETF data and trying to find any kind of patterns or regime detections, things like that. And, you know, they kind of just let me try stuff. A lot of it, I think, is just throwing things at the wall and seeing what sticks.

 

And I’ll also say, as an aside, if there’s anything that I say that you don’t know, or if there’s vocabulary or anything like that, just let me know, and I can explain it a bit more.

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The Role of Programming in Financial Engineering

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Q: How important is having a strong programming background for financial engineering?

 

A: Yeah, so I think financial engineering brings together things like calculus, stats, probability, and a lot of coding as well. I think for a lot of the underlying concepts, you need to have a good understanding of the mathematical background, but to put it into practice—especially—it’s really important to know coding.

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And especially with all of the market data that comes out that you can grab from Bloomberg or Yahoo Finance or… I mean, there are a million different places to grab it from—you kind of need to be able to, I guess, at least feel comfortable coding.

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You don’t need to be a software developer by any means, but you need to at least be able to work with data and understand the ins and outs of what you're trying to do.

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You at least need to be competent. You kind of need to understand what’s going on—you know, it’s not going to do everything for you.

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Future Opportunities

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Q: How do you see AI impacting this field already, and how do you think it’ll continue to impact the field of financial engineering in the upcoming years?

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A: ​Yeah, so there are a lot of different ways. I would say a big part of financial engineering—one piece of it—is algorithmic trading. That’s people who look for different signals, which is just, say, if the market went down today, maybe you buy because you expect it to go up tomorrow or something like that. As they get more sophisticated, a lot of times people can’t really handle all that data by themselves, so you need to use more sophisticated machine learning models to notice patterns that the human eye can’t just pull out of data.

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I think that’s a big part of how people use it: as machine learning models are getting more complex and in‑depth, neural networks are coming into play. A lot of the LLM architecture is being used, because LLMs are basically just predicting the next word that’s going to come in a sentence. If you think about it, stock prices per day are like words, and then the LLM could be used to predict the next stock price. That’s a super simple explanation, super basic, but it’s kind of the main idea. It’s really just that these newer models are getting better at predicting and giving people more tools to build their prediction models as well.

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Advice for Students

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Q: Any specific advice for someone interested in your field, and the kind of work you’re involved in?

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A: Yeah. If I could’ve had this advice when I first started—or a long time ago—it probably would have helped. Like I said, get good with coding, get good with math. Things like Claude Code are pretty helpful. A lot of the big hurdles are just the interviews: brainteasers, riddles, probability questions, stats questions. They’re going to ask you a bunch of these seemingly random things that you kind of just need to know how to do, so all of that is foundational.

 

But I’d say it’s also really important that you don’t neglect the human element. Even though a lot of this is math‑ and science‑based, the world still is human‑connection‑run. If you want a job, you get the most from knowing people, networking, having a friend or a contact at a bank or at a hedge fund, just to get your foot in the door. You could be the smartest person in the world, but if you don’t talk to anybody, if you don’t go outside and nobody knows it, you really can’t do too much.

 

I’d also say if you really want to get into it, it’s important to start to build your own trading strategies if you can. They don’t have to be really that good, but just showing people that you’re doing something on your own, that you’re trying to pursue this and you have some ideas you’re working on—that shows you’re interested enough in the industry to do it on your own. One thing, you can have it on your résumé. Second, when you’re networking, you can talk to people about it; you can say, “Hey, I have this idea, can we bounce some ideas off of each other?” It also just helps you learn some things on your own as well, because a lot of knowledge comes from trying and failing and trying again. Sorry, that was a long answer.

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Skills for Financial Engineering

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Q: What do you think are the main skills required for financial engineering, both technical and personable?

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A: Yeah, so this was something that I kind of noticed—I need to put this in an okay way. I think a lot of engineers have this stereotype of not really being able to explain things to people. They believe a lot of things are kind of trivial. I had a friend in school who was really smart, but he wasn’t really able to explain his thought process, and that’s a really big problem, to be honest.

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You can be an amazing trader and have a really great background, but people still need to invest in you. If they can’t get comfortable with what you’re saying to them—if you can’t put it into words they can understand—they’re not going to want to give you money, frankly. So I think that’s really important. Also, just being inside of a company, it doesn’t matter if it’s a hedge fund, a bank, or an advertising company: being able to talk to people, be friendly with people, be somebody that people want to come to and want to sit next to for eight hours a day is really important. In general, I don’t think that’s ever going to go away. It’s not really unique to finance or financial engineering.

 

Expanding on the Columbia Master's in Financial Engineering ​

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Q: This is more about your education, but can you describe the value of your master's in financial engineering at Columbia, if there are any interesting courses, or just overall?

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A: Yeah, so I’ll say the entire thing was actually really interesting to me. I had done math in undergrad—math and economics—and I minored in astrophysics. One thing I had never done before was something called stochastic calculus. A lot of financial engineering has this stochastic piece, basically just a random‑walk element, because stock prices are often modeled with randomness involved. There is calculus that you can put on top of that randomness piece, if that makes sense. It was this new area of math that I just hadn’t seen before.

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Learning to deal with it and seeing a lot of the different ways that people interact with markets and the ways that math interacts with markets was really interesting. There were different theorems and whole areas of math that I hadn’t even known about that were related to finance. I hadn’t realized that it was so math‑heavy, and that’s what drew me in even more, because it wasn’t really a business course. It wasn’t like a case study on bad publicity and what you should do in response to an employee saying something that people don’t like. It was very much engineering‑heavy, just applied to finance. That’s what I loved about it. The last thing I’ll say is that even though it was financial engineering, a lot of what I got out of it I can apply to other things—like data science, data analyst roles—because a lot of the underlying skills are very applicable in other places.

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Expanding on Coinbase Work​

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Q: How did this education connect to your current work at Coinbase?

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A: Yeah, so for my current work at Coinbase—my contract is up soon—but I’m a data engineer. A lot of what I do is working with SQL and different databases. A lot of it is, I don’t want to say debugging, but if there are any problems with data quality—upstream, downstream tables—making sure that we’re not missing information, making sure there are no duplicate rows, things like that.

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What I learned in school wasn’t really, I’ll say, seemingly very applicable, but the ability to dig into data was something we really focused on in a few of the classes in the financial engineering program. That was something I was able to take from there and bring into the data engineering role. If nothing else, I think just an interest in working with data and with large datasets—which are kind of common now—was something I was able to bring over.

 

What Careers are Available from a Financial Engineering Education

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Q: Yeah, so in your search for careers, what are the career opportunities that you’re interested in with your background in financial engineering?

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A: Yeah, so for me personally, I’d really like to be in some kind of quant research role—basically trying to find those signals of when to trade, when not to trade, how much to trade, things like that.

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But if people want to get into it, there are a lot of different options. There’s quant research, and there’s also the trading side of things. If you want to be more of a software engineer, there are quantitative developers who work on the actual trading systems. There are people in banks pricing derivatives and options and exotic derivatives—I can explain those more if you want me to. There are market makers like Citadel. There are a bunch of different things. And like I said before, even if you’re not in the financial engineering space, you can still take something from it. Going into some kind of AI research field or something like that, there’s still that base that you can use.

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Career Reflection

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Q: Is there anything that you would have done differently in your education or anything else?

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A: It’s hard to say, because I feel like being in real estate for about three and a half years before getting my master’s was the reason why I first got interested in financial engineering. I wouldn’t have had that push if I hadn’t gone into real estate finance. But obviously, with hindsight being 20/20, I think I probably would have started much earlier, maybe right out of undergrad.

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I probably would have done the things I mentioned a few minutes ago—networking, building my own portfolio, things like that—earlier. You can do it while you’re in graduate school, but even then it might be a little bit late. If you can get those years from high school, undergrad, and a little after undergrad all together to work on math and stats and programming and networking, then you can have a much more solid foundation. It’s much more solid than spending a year trying to get it all done before going into the workforce. So yeah, I think just starting everything earlier. But at the same time, I had to take the wrong path to figure out the right path, so I can’t really say that it was wrong or that I would change it, if that makes sense.

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Connection between Real Estate and Financial Engineering

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Q: Can you expand a little bit on how real estate got you interested in financial engineering?

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A: Yeah, definitely. It wasn’t really the real estate itself. I think it was that real estate is very old‑fashioned, at least where I was working. A lot of it was just based in Excel, and I was the only one at my company that knew how to use Python or was really using VBA or anything like that. The lack of quantitative methods I was seeing made me want more and made me miss a lot of the things I had done in undergrad, where I majored in math.

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That got me interested in exploring other possibilities. There are things like investment banking and fundamental equity research, but I feel like those are also not the most quantitative. They’re very relationship‑driven, or, in the case of investment banking, a lot of PowerPoint—fixing colors and aligning boxes or whatever.

Quant finance was really the thing that seemed to be the North Star for quantitative work. It obviously has a strong quantitative aspect to it. So yeah, I think it was real estate that got me looking for something more numerically heavy.

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Future Goals

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Q: What are your current short-term and long‑term goals that you see for yourself?

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A: So, the startup—not really, that was just the internship, I was just fortunate there. Long‑term goals, I’d say, would be to do something on my own. I don’t need to open up my own shop or anything like that, but even if I’m not able to find a job in quant finance or I don’t go down that path, I’d like to at least build something on the side—my own portfolio—and be able to trade myself, just so I can keep up with those skills and use them as much as I can.

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I always say that long‑term, I want to be the person people go to when they have a question on anything really—whether that’s a certain trading signal or a coding question or something like that. That’s my long‑term goal: to be somebody who is known for being that go‑to person. Obviously I have some work to get there, but that’s the idea.

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Favorite Part about Work

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Q: What is your favorite part about financial engineering in general?

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A: I would say that my favorite part is the difficulty. I think I would rather struggle through a hard problem than go through easy problems that you can get through in a minute or two, where you’re left without any real satisfaction. Whereas if you have a hard problem and you’re struggling through it, and you step away from it, have a few ideas, and then run back to it, I think that’s a lot more satisfying and a lot more fun. That’s what really draws me in a lot more. Hopefully, that answers your question.​

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Connection between ML and FE

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Q: How much did you work with machine learning in financial engineering?

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A: Yeah, so when I was there, I did take two classes on it—there was that independent research class, and I know there was another one, I think it was literally just called “Machine Learning for AI.” I probably could have taken one more, but I think a lot of the underlying machine learning models were at least talked about.

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When I was there, I think that was when ChatGPT really started to blow up, so I imagine that over the next few years Columbia is going to be changing their program a bit more to incorporate that kind of stuff. We did talk about neural networks and PyTorch and machine learning and different statistical models that use machine learning and stuff like that, so it was definitely an underlying piece of it. But at the same time, a lot of what we talked about was the fundamentals—why everything works, how it fits together—and then the machine learning came on top as a way to make all that work, if that makes sense.