Latest / The Career Change Podcast with Fexingo: Switching Fields, Bootcamps, and Reskilling / How Bootcamp Grads Break Into Quant Finance
Transcript
- Lucas: So last week I found myself on a call with a friend who runs a two-billion-dollar quant fund, and he said something that caught me off guard. He said the hardest roles to fill right now aren't the PhD physicists — they're the people who can actually code and think about markets at the same time. Luna: Wait, that is surprising. I always assumed quant shops only hire from Ivy League math programs. Lucas: That's the old stereotype, but the reality is shifting. A lot of the quantitative work is becoming more applied, more data-heavy, and frankly more accessible. Which is why today I want to look at how a bootcamp grad actually breaks into quant finance — not the theory, but the real path. Luna: And we have a perfect case. Marcus Chen, a former bootcamp student we interviewed last month, spent two years getting from a coding bootcamp to a quant research associate role at a mid-sized hedge fund. Lucas: Right. And what's interesting about Marcus is that he didn't come from a math background. He studied economics as an undergrad, worked in operations for a few years, and then did a twelve-week Python bootcamp. So his route is genuinely non-traditional. Luna: And what did he say was the biggest surprise about the hiring process? Lucas: He said the technical interviews were less about deriving formulas and more about data manipulation and problem-solving. He remembers one interview where they gave him a messy CSV of transaction data and asked him to find anomalies. It wasn't about fancy math — it was about being systematic with pandas and numpy. Luna: So the skills you learn in a bootcamp — data cleaning, structuring code, debugging — those are actually relevant. Lucas: Exactly. Marcus said that in his first six months on the job, he spent about seventy percent of his time on data pipelines and the rest on research. The PhDs on his team would come up with models, but someone had to make the data usable. That was him. Luna: But there's still a knowledge gap — he must have had to learn finance from scratch. Lucas: He did. And this is where the bootcamp grads can actually outshine people with pure tech backgrounds. Marcus self-studied with online courses on options and futures, he read a few classic books on quantitative trading, and he built a small personal project where he backtested a simple moving average strategy. He could talk about that project with genuine enthusiasm. Luna: And that project is what got him the interview in the first place, right? Lucas: It did. He told us the hiring manager later said it was his project on GitHub that stood out, because it showed he could apply code to market data. So the bar isn't as high as people think — but you have to prove you can work with financial data specifically. Luna: It sounds like a very practical skill set. But what about the math? I've heard quant interviews are full of calculus and linear algebra. Lucas: Marcus said the math that actually matters for his day-to-day is mostly statistics and basic probability. He took a refresher course on linear regression and time series. But he emphasized that you don't need to be a math Olympiad winner. What you need is to be comfortable with concepts like correlation, volatility, and statistical significance. Luna: That sounds like something a bootcamp grad can learn with focused effort. Lucas: Totally. And that's the crux of this episode — the quant world is more open than it used to be, but you have to be strategic about how you enter. We'll break down the exact steps Marcus took, the skills he prioritized, and the mistakes he made so you don't have to. Luna: I'm in. Let's get into it. Lucas: So first, the landscape. When I think of quant finance, I think of two main tracks: the sell-side, like at big banks, and the buy-side, like at hedge funds and asset managers. Marcus is on the buy-side, which is generally smaller and more tech-driven. Luna: And which track is more receptive to bootcamp grads? Lucas: He said the buy-side, especially the smaller funds, are more willing to take chances on non-traditional candidates because they need people who can build things quickly. The big banks still have HR screens that filter for degrees, but a fund with fifty people can just talk to you directly — and if you can do the work, they'll try you out. Luna: So the key is to target firms where the hiring manager has more discretion. Lucas: Exactly. But before you even get to the interview, you need to speak the language. Marcus said he spent a full month just learning the basics of financial markets — what an order book is, how market makers operate, what a ticker symbol actually represents. Not deep theory, but enough to hold a conversation. Luna: And that's where a lot of bootcamp grads trip up — they have the coding chops but they don't know what a 'limit order' is. Lucas: Right. So he recommends three specific resources: the 'Options, Futures, and Other Derivatives' textbook, but only the first few chapters, a free online course on financial markets, and the Financial Times — just to get comfortable with market commentary. It's about building background knowledge, not becoming an expert. Luna: And then the technical skills. What does the actual job require beyond Python? Lucas: At Marcus's fund, the stack is Python, SQL, and a bit of R. He said the most important thing is to be fluent in pandas and numpy, because you'll spend a huge amount of time manipulating time series data. Version control with Git is a given, and they also use AWS for data storage — so knowing basic cloud services like S3 helps. Luna: What about machine learning? I keep hearing that quant roles are all about ML now. Lucas: It's becoming more common, but Marcus said the majority of his work is still statistical analysis and data exploration. He does use scikit-learn for some models, but he didn't need to master deep learning to get the job. That said, having a solid grasp of regression, classification, and feature engineering is a differentiator. Luna: So it's more about the fundamentals than the flashy stuff. Lucas: Exactly. And now, this is a good moment to mention something we don't usually do on this podcast, but it's important. We keep this show completely ad-free, and that's a choice we've made from day one. If you've found episodes like this helpful in thinking through your career, and you want to support that choice, you can find us at buy me a coffee dot com slash fexingo. It's a simple way to keep the show independent. Luna: Yeah, and every contribution, even a small one, really helps us keep doing these deep dives. We appreciate every person who's ever sent something our way. Lucas: Alright, back to the path. So after Marcus built his finance knowledge, he started working on a portfolio. But he didn't just clone a tutorial. He built a project that was genuinely about a question he cared about: whether a simple moving-average crossover strategy works on tech stocks. He used Python to pull data, backtest it, and he wrote up the results in a clear blog post. Luna: And that's the kind of project that shows you can think independently. Lucas: It does. He also made sure the code was clean and documented. He said he spent almost as much time on the README as on the code itself, because he knew hiring managers would look at it. That project became his calling card. Luna: But projects alone don't get you an interview at a hedge fund. How did he actually get the meeting? Lucas: This is where networking gets specific. Marcus didn't send cold emails to random firms. Instead, he found meetups and online communities for quant enthusiasts. He joined a Discord server for algo traders, contributed to open-source projects related to finance, and he attended two in-person finance meetups where he talked to people working in the industry. Luna: So it's about being visible where practitioners hang out. Lucas: Exactly. He said one of those meetups led to a conversation with a data engineer at a fund, who later passed his resume to the hiring team. It took about eight months of consistent networking before he got his first phone screen. Luna: Eight months is a long time. Was he working full-time during that period? Lucas: He was. He had a job in operations at a logistics company, and he spent his evenings and weekends on his finance study and his project. He said the key was to set small, achievable goals — like completing one module of the options course per week, or writing one blog post per month. Luna: So it's a marathon, not a sprint. But what about the interviews themselves? What was the format like? Lucas: Marcus went through three rounds. The first was a phone screen with a recruiter, mostly about his background and motivation. The second was a technical interview with a quant, where they asked him to code a simple trading strategy in a shared editor. The third was a full-day onsite where he met the team and did a case study presentation on his project. Luna: That sounds intense, especially the live coding. Lucas: He said it was nerve-wracking, but the key was that they weren't looking for a perfect solution. They wanted to see his thought process. He made a mistake in his code but explained his reasoning out loud, and that was enough. He also said that practicing with a friend who was a software engineer helped a lot — they did mock interviews every weekend for a month. Luna: And then he got the offer. So what advice would Marcus give to a bootcamp grad who's thinking about this path? Lucas: His number one advice is to be honest about your learning journey. Don't pretend you know finance when you don't. Instead, show that you're a fast learner and that you've already taken the initiative to learn the basics. He also said to focus on the buy-side, because the sell-side still has a lot of credential bias. Luna: And if you're not near a financial hub, is it still possible? Lucas: He thinks so. Many funds are now remote-first or hybrid. He works in New York, but he knows people who work for funds from smaller cities. The key is to be flexible and to make yourself visible online through GitHub, blog posts, and communities like the one he joined. Luna: So the path is clear: build a finance-specific project, network with practitioners, and target smaller buy-side firms. Lucas: That's the short version. But I want to stress that this isn't a quick win. Marcus spent two years from bootcamp to offer. He had setbacks — he was rejected from more than a dozen positions before his first interview. But he kept iterating on his project and his networking, and eventually it paid off. Luna: It's reassuring to know that persistence matters more than pedigree in this niche. Lucas: I think that's the takeaway. The quant finance door is ajar, and bootcamp grads who are willing to invest in finance knowledge and show real passion can walk through. So if you're a bootcamp grad looking at finance, maybe it's worth a shot. Luna: And if you've got a question about breaking into finance, let us know. We might feature it in a future episode. Lucas: Until next time, keep building.