Sorry — We Mainly Recruit Math Majors

“Do you have any math competition awards?”

That was my first real interview for a quant-related role. I walked in with a resume listing the trading systems I’d built, my investment track record, and the coding projects I’d taught myself. The interviewer glanced at it and asked me that.

I said no.

He flipped through the resume. “What was your undergraduate major?”

Economics.

“Sorry — we mainly recruit from math, statistics, and computer science.”

The conversation lasted maybe five minutes. He didn’t even ask what I’d done.

What Economics Gave Me

Honestly, I don’t regret studying economics.

It taught me a particular way of seeing the world — every choice has a cost, every decision is made under uncertainty, and every seemingly irrational outcome has an incentive structure driving it.

These ideas sound abstract, but once they’re internalized, everything looks different. You see a product go viral and you don’t think “that’s impressive” — you think “what incentive structure makes users want to share this?” You see a market move and you don’t think “it went up” — you think “what expectations is this price reflecting?”

What economics gave me was an operating system for thinking.

What Economics Didn’t Give Me

A label.

CS graduates walk out and the world says: you can code. Math graduates walk out and the world says: you have hard skills. Statistics graduates walk out and the world says: you can work with data.

Economics graduates walk out and the world says: what exactly can you do?

No single hard skill you can put to work on day one. No course that makes you job-ready the moment you graduate. You studied supply and demand curves, game theory, macro models — but open any job board and not a single posting says “Requirements: understands marginal analysis.”

You possess an extraordinarily valuable way of thinking, but it’s invisible. It can’t be listed in the skills section of your resume, it can’t pass an HR keyword filter, and it can’t hold up when someone asks you “what’s your specialty?”

Hitting the Wall

I tried to break into quant.

The field sits naturally at the intersection of finance and technology — I thought that was exactly where I belonged.

But reality looked like this: open any quant job description and the education requirement reads math, statistics, computer science, physics. Economics? Not on the list.

I self-studied. Ground through probability theory on my own, wrote my own backtesting framework, ran my own strategies. But no matter how much you learn, the major on your resume doesn’t change. The screening happens before anyone sees what you can do.

That period was suffocating. It wasn’t that I couldn’t do the work — it was that nobody would let me try. You know you’re capable, but the world won’t give you a chance to prove it.

That’s more painful than lacking ability. If you lack ability, you can train. If you lack credentials, there’s nowhere to train.

What They Don’t Know

When that interviewer asked me about math competition medals, he probably didn’t know the following.

I know which problems are worth solving.

Math people can solve equations. CS people can build systems. But they’re often solving a problem nobody needs solved. I’ve seen too many technically flawless projects pointed in completely the wrong direction — beautiful models that nobody would pay for. Economics taught me to think before building: does the incentive structure make sense? Why does this market work the way it does? Is it even worth doing?

I know why models break.

A pure-math quant sees a statistical arbitrage signal and bets on it. I ask one more question: why does this spread exist? Is it risk compensation or genuine mispricing? Will it show up again? Economics trained me to think about the causal mechanism behind the model, not just the correlation. Pure-math quants blow up during regime changes because their models don’t understand “why” — they only know “what.”

I’ve lost real money.

Many of those math PhDs who go straight onto a quant desk have never traded with their own capital. They understand volatility as a number. I understand volatility as the churning in your stomach at 3 AM watching your account shrink. A 15% max drawdown in a backtest report is one line of text. Carrying it on your own shoulders is an entirely different experience. My investment discipline wasn’t taught in a classroom — it was forged with real money.

I can build the whole thing.

A math person writes an alpha signal and needs a CS person to build the system and a product person to design the interface. I can go from strategy logic to code implementation to data pipeline to visualization — end to end, by myself. As a teenager I built a quotation system and an order management platform for my family’s foreign trade business. Now I’ve built an entire personal website with a quant-terminal visual language — Three.js scenes, bilingual content system, deployment — all by myself. A pure CS person can build it but doesn’t speak the language of finance. A pure finance person speaks the language but can’t build it.

I can translate complexity into plain language.

When pure-math people explain a strategy to a PM, it’s all Greek letters and the other side understands nothing. I can translate a complex model into business language, because my starting point is economic intuition, not mathematical formalism. This isn’t “good communication skills” — it’s the ability to switch fluently between the contexts of technology, finance, and business.

Proving Them Wrong

My mindset right now is simple — prove them wrong.

Not anger. A quiet obsession. You won’t let me through the door? Fine. I’ll build my own.

This website is one piece of evidence. My trading system is another. Every line of code I write, every project I ship, every article I publish — all of it is accumulating something that can’t be captured on a résumé.

The compound curve for a generalist looks different from a specialist’s. A specialist’s curve is steep but narrow — one line going up, visible almost immediately. A generalist’s curve is flat but wide — many lines rising slowly, until one day they suddenly intersect, stack, and explode.

The problem is, before the intersection, you’re nothing.

You’re not the best programmer, not the strongest mathematician, not the most credentialed trader. You can’t pin a single label on yourself. In a world that demands you define yourself in one sentence, that’s brutal.

Still on the Road

If you asked me — would you choose CS if you could start over?

Honestly, I don’t know. Choosing CS would mean admitting that interviewer was right — that labels matter more than ability, at least at the gate. But not choosing CS means I’m betting on a harder path — betting that the compound interest of being a generalist will eventually pay out.

I don’t have the answer yet.

I just know I haven’t stopped.