Transcription
Listen up. You want to be a quant. You've seen stories of people at Citadel or Jane Street making $500,000, $600,000 a year out of college and millions in their 20s and 30s doing math problems that look like they were designed by aliens. And you're sitting here thinking, "Wait, how can I do that?"
Well, today I'm giving you the complete blueprint step by step by step on how to break into the quant field, even if you've got zero connections, zero experience, and no IV League name on your resume. My name is Aman. I'm a former software engineer and career coach who's worked at companies like Amazon and Shopify. After I graduated college, I was able to turn six software engineering internships into multiple six-figure offers by age 22.
Now, full disclaimer, I'm not a quant. I didn't go down that path myself. But I've interviewed two absolute mathematical weapons who did. First, my younger brother Ado. At 20 years old, he landed a $600,000 quant research offer at Jump Trading. He's been obsessed with math since middle school, grinded out 30 quant interviews in 3 months, and got return offers from places like Jump Trading and IMC Trading.
And then there's Sunny, another friend of ours who turned down a $500,000 quant job at Headlands. He's done everything from software engineer internships to academic research to working at startups to building machine learning projects in high school and landing some of the best companies in tech.
So, in this video, I'm breaking down their strategies step by step by step. What they studied, how they built their resumes, what resources they used to go from realizing they like math to their offer letter from a company like Jane Street. Let's get into it.
All right, before we dive into the step-by-step blueprint, let's clear one thing up. What the hell even is a quant? You've probably heard the word thrown around in movies like The Big Short or shows like Billions, where everyone's wearing suits, slamming phones, and throwing around words like alpha, volatility, or market neutral like it's a personality trait. But being a quant is way less about yelling across trading floors and way more about the fact that at a high level you use math and coding to make trading decisions. Quants build models that move billions of dollars based on probabilities, stats, signals, and patterns in the market.
There are a few different roles in the space, but the two big ones are quant researchers and quant traders. Now, quant researchers are the brains behind the strategies. They build and test statistical models that can predict how markets behave. Think about quant research like using math to find hidden edges in financial data. It's slower pace, more research heavy, and super technical.
And then we have quant traders, who are the folks that take those models and execute real trades in real time. It's fast, chaotic, and all about speed. You're constantly optimizing, tweaking models, and making snap decisions, sometimes with millions of dollars on the line.
And then there's quant devs, which just means software engineers who build the infrastructure and tools to make all of this quant magic possible. They're not creating strategies, but they're the reason the strategies work in the first place. But here's the common thread across all three. You've got to be really good at solving hard problems fast. That's the baseline. You don't need a finance degree or a business degree. What you do need is strong fundamentals in math, statistics, and programming.
Now, where did the quant field even come from? Let's go all the way back to the 1980s and '90s. Before the quant revolution, Wall Street was straight up old school. Think suspenders shouting across trading floors and guys with slick back hair making million-dollar bets over lunch. Trades were done on gut feel. Market intuition and a rolodex of the right people to call. If you could talk fast and golf well, you basically had the job locked down.
But then the game started to change. In the late 80s, firms like Renaissance Technologies kicked off a new era by bringing in physicists and math PhDs to build serious statistical models, not just finance bros and rolodexes. Citadel and jump trading came a bit later in the 90s, adding fuel to the fire with their own high-powered tech and quant strategies. They weren't yelling into phones, they were running code instead.
Now, here's a tech part. Back then, Python wasn't the go-to. It barely existed outside academia and research. These firms were mainly coding in low-level languages like C and C++. Fast, efficient, and close to the metal. No sleek Python scripts running automated back tests like today. It was hardcore programming optimized for speed.
Fast forward to now and Python has exploded in popularity, powering everything from data pipelines to machine learning models on the trading floor running 24/7. The ad stopped being about who you knew and became about who could process and act on information faster than anyone else. And today, quants are running the show. More than half of all trading volume in the US markets is now algorithmic. Meaning it's driven by math and machines, not humans. Firms like Jane Street, IMC, and Two Sigma are competing to squeeze out microscopic edges using data, research, and tech.
And that's exactly where modern quant internships came from. These aren't coffee fetching gigs. You're actually in the code, in the research, working on the same problems the full-timers are solving. The pay is insane, the learning curve is brutal, and the bar to get in is high. But if you prep smart, you can absolutely get there. You're literally using code and math to predict the markets and make decisions that move millions of dollars. And if you think it sounds intense, yes it is. But the craziest part, quant is one of the most meritocratic fields ever. If you can solve hard problems and think clearly under pressure, you can get a job as a quant. No old boys club, no needing to know a guy, just skill, preparation, and execution.
Now, how do you do that? How do you go from I don't even know what a quant is to working on a trading floor with some of the smartest people in the world? Well, let me break down a step-by-step blueprint I like to call from zero to trading floor, the quant blueprint.
And the first principle is simple. Let me tell you about Ado, my little brother. Now, this guy was state champion science bowl captain, crushing math, physics, and chemistry. And it worked. Back in 8th grade, he was deep into math counts, a tough middle school math competition, where he went from being runner up to state level contender after grinding hard for weeks, putting in hours like a total beast. That moment lit a fire underneath him. He studied his butt off, got private coaching, and nearly made it to nationals. That kind of drive early on, it shaped everything.
High school didn't slow him down earlier. He and I even took over our school's math club. A hostile takeover, if you will, turning a tiny, sleepy group into something serious with after school problem sessions in our basement. And the summer math camps. Yeah, I'm talking about the kind where you study graduate level math alongside kids way smarter than you from across the country. He went to places like Proven Math Academy in Colorado and the Hampshire College summer studies in mathematics. Intense stuff that crushed his confidence at times, but also pushed him to level up. He learned what it really means to compete with the best.
But here's the thing, you don't need to start that early. Quant trading isn't your regular Wall Street gig. You're not cold calling or schmoozing clients. You're doing technical research or software work all day under pressure. And the better you are at breaking down complex problems fast, the more dangerous you are in this game, which is exactly why Adel's story is so interesting. Let's hear what Adel has to say about his math obsession.
>> This is one of the big moments for me where I realized I'm good at math. I can work hard at something for 6 weeks and be one of the top people in the state. We do like some common dorks problem. I figure it out. Tell him how I I tell him the answer. He's like, "Oh, that's right." And then he looks at me, he's like, "How the hell did you do that?"
>> Cuz as a context, Aldo's like a 13-year-old.
>> Yeah. I'm like 13. This >> 13-year-old. This guy's like 17 or 18.
>> I'm like some weird 13-year-old.
>> Weird and nerdy 13-year-old who shows up out of nowhere and just like >> does this problem, right?
>> Yeah. I applied to this summer camp called Prove It Math Academy. And it was a two-week summer camp in Boulder, Colorado, where they got all these high school kids to come and study pure math for 2 weeks. I found it really interesting. I really enjoyed it. And that was the first time I really I met kids that were significantly ahead of me cuz again, you're from Iowa. You're not going to meet anyone that that is so far ahead of you. You go to these math camps, there's these kids from the Bay, from Texas, from New York, from New Jersey, and they're they're probably 10 times more experienced in the field than you are. So, that kind of motivated me to study a little more, drove me to apply to more math camps and experience that again. But overall, I really enjoyed that. Really enjoyed learning about uh pure math.
As you can see, he wasn't just doing school assignments. Adel was studying graduate level math at summer camps for fun and solving competitive problems, which laid the foundation for thinking fast and precisely. Now, again, you don't have to be like Adel. You just have to have Adel's level of seriousness.
So, based on what stage you're at, how do you actually get started? If you're in high school or early college, you need to focus on your fundamental math skills. Let's go over to Sunny, for example. Now, Sunny didn't start with finance earlier. He just got really good at understanding technical concepts. The very first thing he did was land a machine learning internship at Northrop Grumman in high school just by being curious, sharp, and knowing how to communicate his ideas.
>> That's how I essentially got my first internship. That was my junior year of high school.
>> Wow.
>> And I would say that at that time I didn't really have too much experience with machine learning. Like I was broadly familiar with what machine learning was, but I wouldn't say that I knew like too much about like oh what's the what are some of the models that we should be using in specific situations or that sort of nature. So I'd say that that was like probably one of the most important things that happened to me also because you know getting your first internship in college without any experience is hard.
>> It's already hard for people to get their internships like sophomore year.
>> Yeah. Junior high school to do that.
>> Yeah. So that that first internship really helps you pick up these other internships later on and you know I'm like really grateful for for having that opportunity because I think it just helped me streamline the process with getting more internships in college and it was just a lot easier.
Now maybe you're thinking cool, I'm not in high school. I'm about to start college so what should I do? This is where most people overcomplicate things. They guess. They take random classes and then wonder why they're not prepared for quant interviews. Here's the truth. There's a core set of classes that will give you 80% of the firepower you need.
Number one, probability or EEK126, which is a class at Berkeley. Don't worry if you're not the next Wall Street prodigy. What quant firms really want is a solid math foundation, especially in probability and statistics. The finance, they'll teach you that on the job, but if you fumble the basic math logic, they'll move on. In order to apply this, you need to prioritize courses and resources that build deep intuition, not just formulas. Adel even mentioned his stats coursework is what gave him an edge. So, master this and you've already separated yourself from 70% of applicants.
>> My studying just kind of consisted of taking my classes in relevant topics such as like Berkeley has this class called EX126, which is in probability. That's probably the most relevant class to the quant interviews.
Number two, machine learning or CS189. This isn't just some flashy buzzword. Machine learning teaches you how to build models that predict and adapt skills that translate directly to quant roles where you're often trying to forecast market moves or spot hidden patterns. The better you understand ML fundamentals, the more you can combine math and coding to build smart trading systems. Plus, it shows you how to handle real-world messy data, not just clean textbook problems.
Number three, data structures and algorithms. Now, DSA is still a huge deal, but don't just grind LeetCode like a robot. Quant interviews throw follow-up questions, twists, and variations. So, if you only memorize the base solution, you're toast. What to do instead is focus on conceptual understanding. Ask yourself why each algorithm works, not just how. That depth of thinking is what separates solid candidates from surface-level ones.
Next up, linear algebra and multivariable calculus. These are the backbone of many quantitative models. Linear algebra helps you understand how to manipulate data in multiple dimensions. Think vectors, matrices, and transformations, which show up everywhere from risk modeling to portfolio optimization. Multivariable calculus, meanwhile, teaches you how to analyze functions with multiple inputs. Essential when you're dealing with complex pricing models or optimizing trading strategies that depend on multiple variables at once. Nail these and you've got the math toolkit that quant firms crave.
Onto the next step. Yes, coding matters. Python and C++ are top choices in the quant world. But here's the thing. It's not about syntax flexing. It's about solving real problems with clean, efficient logic. They care about how you think, not whether you know the quirks of list comprehensions. Ado literally passed a Jump Trading interview because they asked him a dynamic programming question and he had seen dynamic programming in algorithms class the week before which helped him figure it out on the spot. So yeah, being intentional how you study pays off.
So I decided to interview at Jump for the quant research internship. I didn't do any preparation but luckily I was taking algorithms at UC Berkeley at Berkeley at the same time and they asked me this uh like a dynamic programming question for the algorithm side of the interview and we just covered dynamic programming a couple weeks ago. So I was able to get it done. The math side I did okay. The data science side I did okay as well. So they decided that I performed well in the interviews and then they sent they gave me an offer to join them for the summer as well for a quant research internship.
Some people think finance classes will help, but they won't, unless they're extremely math heavy. Focus on thinking probabilistically, coding efficiently, and optimizing algorithms. That's what trading firms care about. So, the bottom line here is that you need deep math and coding muscles and the ability to solve hard problems fast. And the earlier you start building that muscle, the sharper it gets. So, if you're late to the game, it doesn't matter. Pick up the ball and start training.
All right. Now you have a rough idea of the basic knowledge you need to be a quant in school. Let's talk about how you actually become one by passing interviews and stuff like that. And no, it's not a course. It's not a YouTube channel. It's not even a secret Discord group. It's a book called a green book and it's your secret weapon. And here's what's wild. When I interviewed both Adel and Sunny, two people who actually landed offers at top quant firms like Jump and Headlands, they both mentioned the same thing. They said, "If you want to break into quant, study the green book."
But there's this book called the green book that a lot of people know about that helps you study for quant interviews. And it goes over like basically almost everything you need to know for quant interviews. So it goes over uh basic probability, statistics, multivariable calculus, linear algebra, different types of problems, you know, like Markov chain, stuff like that. How to apply those concepts and solve the questions.
>> There's a book called the green book. It's not called the green book. It's called uh a practical guide to quant finance interviews I think but it's commonly called the green book. That's like one of the I guess holy grails when it comes to quant finance. You can really just self-study the entire book and do some practice problems from um from online and you should probably be good enough to like take a quant finance interview and and probably pass. As long as you really practice and strengthen your fundamentals and problem and statistics, you should be uh good to go on that end.
Now, at this point, you're probably thinking, "What the hell is the green book? It sounds like a high school chemistry notebook or something like that." But no, it's actually a PDF called a practical guide to quantitative finance interviews. But in the quant world, everyone just calls it the green book because of the green cover. And trust me, this book isn't just some casual reading material. It's the quant bible. If you're walking into a Jane Street interview and you haven't touched the green book, you're already playing on hard mode.
Inside, it's packed with real quant interview questions. We're talking brain teasers, probability, mental math, Markov chains, linear algebra, finance puzzles, calculus, and more importantly, it teaches you how to think. That's the whole game. Quant firms don't just care about what formulas you've memorized. They care about how you attack a weird abstract problem when the clock's ticking and your brain's like, "Uh, what?"
So, how do you actually use the green book so it changes the way you think? First, don't just flip to the answers like it's a high school math textbook. The magic is in the struggle. Pick a problem, stick with it, and wrestle it down yourself first. Even if it takes 20 minutes and you feel stuck, that's literally building the exact mental muscles you'll need in interviews. Second, use pen and paper. Why? Because your brain processes problems differently when you physically write them out. You see the structure, you spot patterns, you slow down enough to avoid dumb mistakes. Plus, interviews won't let you whip out ChatGPT. It's going to be you, a marker, and a whiteboard.
Now, once you've got a handle on the process, it's time to add the pressure. Time yourself. Give yourself 2 to 3 minutes per problem like it's a real interview. It forces you to think clearly fast, which is exactly what trading floors feel like in real life. And finally, build up in layers. Start slow, get the accuracy down, and then ratchet up the pace. The point is to get so comfortable under pressure that when the real interview comes, you're basically in green book autopilot mode. Master this book and you're already 10 steps ahead of the average applicant who just skimmed through it.
Now, once you master the green book, you've taken these probability and statistics classes, these math classes. How do you actually master the quant interviews? Well, the secret is what Aldo says next.
So, I did some research and I was like, "This sounds cool." Like, it's kind of like a combination of math and computer science, but you're applying it to make trading algorithms and stuff. So, I applied to a bunch of quant internships my freshman year freshman year >> of uh college. Okay. And I had all the alpha from like there's another Reddit called r/cs majors. So I would read all those posts. So they're like dude if you're a freshman they'll just autoreject you. So I was like okay let me just lie about my grad date and say I'm a junior so I can like get these internships. So for Amazon I lied and said I was a junior and I was like I calculated I was like okay like if I took four or five classes this semester I could graduate in 2 years. So I'm not even lying. the interview stages I got to um like the OAS I took everything.
>> So throw some like rough numbers out here.
>> Yeah. So that spreadsheet had about 200 companies I applied to. I did about 50 to 60 interviews total.
>> Wow.
>> Over the course >> interviews like you talk 50 to 60 different interviews. Not even OAS.
>> Yeah. Interviews.
>> Yeah.
>> Over the course of let's say July to October. So how many months is that? Three and a half. Three months. So 60 interviews in 3 months. That's like they're only on weekdays, right?
>> Yeah. So that's 12 times 5 60 week days. On average, one interview a day.
>> One interview straight.
>> For 3 months straight.
>> So I applied to a bunch of quant internships. Applied to a bunch of software engineering internships. And I ended up interviewing at a company called Jane Street.
So you work at Jane Street. People like bow down to you. Now, if your goal is to become a software engineer at a quant company, the kind of place that demands both razor sharp coding skills and deep quant knowledge, then you need more than just self-study. And that's exactly why we created the software engineering accelerator. This program is designed to fast-track you from zero to interview ready with hands-on coding practice, mock interviews, and a curriculum built around the exact software engineering skills quant firms are looking for. No guesswork, no random tutorials, just a proven road map, mentorship, and real-world problems that prepare you for the actual grind. So, if you're serious about breaking in, this accelerator is your best shot at cutting through the noise and landing your dream role.
All right, now let's talk about the thing that most people never do, which is applying before you're actually ready. Adel did this in his freshman year when he applied to over 200 companies. The dude got rejected left, right, and center. So, what did he do? He changed freshman to junior in his resume. Now, don't do that unless you can technically pull it off based on the amount of credits you have in your degree. But here's the deeper principle. Don't wait for someone to qualify you. You've got to throw yourself in the game early. Although didn't wait around for the perfect GPA or perfect coursework on his resume, he faked it until he made it and ended up landing a John Deere internship through a referral. That internship was his foot in the door. So, what's the lesson here? Apply wide. Talk to people and use your network, even if your network is just your friend's uncle's neighbor's cat. Connections count. Every shot you take expands your surface area of luck.
Now, here's the part most people screw up, which is when it comes to interview prep. Aldo got final rounds at IMC, Optiver, Akuna Capital, Two Sigma, and Virtu. Like crazy impressive names, but he bombed most of them. Why? Because he didn't take interview prep seriously. He was just out here winging it, hoping his pattern recognition from classes or contests would carry him through. Big mistake.
>> So, tell me about like your success. Yeah. So, there's a reason why I had to do 60 interviews. I was still stupid. I didn't study for any of them. This guy's going blind all these different shotgun approach instead of the sniper approach. Okay, which I mean if you don't have too much experience on your resume, you need to combine them. You need to take a shotgun and then snipe the ones that give you an opportunity. But yeah, I applied to a bunch of software engineering internships, bunch of quant internships, and I never like explicitly studied for the quant interviews, but I did so many of them that my study time was just doing the interviews themselves. So I would like see common questions getting asked, like common patterns, and I just got better by interviewing like itself instead of studying. If I could go back, I probably would have said like, "Dude, you don't need to interview at six. You don't you don't need to do 60 interviews. Just study the content. Pick a couple that give you an opportunity and just do well on those." So then you save your time, but obviously I didn't know that.
>> Hindsight is 2020.
>> Yeah, exactly. I interviewed at Akuna Capital for an internship and I got to the fourth round. These companies have a lot of rounds, okay? Like they had two online assessments, then a phone screen, then I had this fourth round and then I bombed again. I interviewed at a company called Five Rings, which dude, their interview is so weird. They asked they asked me all these Fermi questions, like you know, like how many elephants do you think could fit in a plane or something like that?
>> Yeah. Like how how much is the biggest elephant weigh? You know, something like that, right?
>> Lots of elephant questions.
>> Yeah. So I bombed that. Um, and then for software engineering, the only company that I can remember, uh, there are two companies. One is Indeed, like the job board, but I was interviewing for business intelligence and they I got to the final round and then I was like, "Hey, I'm more interested in data science." And I did some coding stuff. They're like, "Yeah, we can tell you you're not really interested in business intelligence, so it's fine. We're not going to give you an offer." And then the other company was Amazon. Um, so I got to the final round of Amazon. Unfortunately, I didn't understand LeetCode and I didn't know that I was supposed to study LeetCode. So they asked me like an intervals question, like a classic like >> merge merge intervals or something. Something like that, right?
>> And I >> very basically code like if I study he would have passed it >> but unfortunately I didn't >> and so I didn't pass that interview and I was kind of stuck.
So here's the exact playbook Albo used to fix this problem. Study the green book, a practical guide to quantitative finance interviews, like it's your bible because it really is for quant. Two, do 100 plus LeetCode problems in a few weeks. And here's the thing, it's not about brute forcing problems until your keyboard starts smoking. You want variety. Arrays, graphs, recursion, string manipulation. But don't skip dynamic programming. For quant Dev questions are like the boss level in the game. They test whether you can take a messy multi-step problem and break it down into smaller reusable subproblems. Most people fail here because they just memorize solutions. That's useless when the interviewer changes one little condition and suddenly your memorized answer breaks. Instead, aim to understand why each approach works, how to tweak it, and how to explain your reasoning out loud.
where I realized I need to study LeetCode for I only studied for about three weeks, but I studied probably two to three hours a day for those three weeks.
>> And what I would do is I would do a bunch of problems in my free time at the gym. I would listen to NeetCode videos like a podcast.
>> Mhm.
>> When I was pooping, I was watching a NeetCode video.
>> Yeah.
>> Everyone has a bunch of like downtime.
>> Mhm.
>> At the gym, in the bathroom, while you're eating,
>> You probably have like 3 hours of downtime per day that you can use.
>> Mhm.
>> And most people are like, "Dude, don't watch the video. You need to do the problem."
>> Like, if you don't do the problem, you're not going to learn as much >> compared to like if you watch a video, you're not going to learn as much as doing the problem.
>> Mhm.
>> Yeah, that's true.
>> But if you're if you're pooping, like, are you actually going to do the problem? It's like, don't let perfect be the enemy of good. It's better to watch a video about LeetCode than do nothing.
>> Mhm.
>> So, for for those two to three weeks, I studied, I did do a decent amount of work, and then after that, I didn't really study. He would read the problem. I would pause it, think about how I would do it, think about how I would code it.
>> How long would you spend thinking about it?
>> Usually I was able to figure it out when I was listen you were you were kind of passively practicing these problems through >> I was practicing it.
>> So I think it's a big deal because I was kind of studying. I just wasn't coding it out.
>> I mean coding it out and debugging is a separate skill, but coming up with a solution is is a different skill.
>> So I was working on coming up with a solution.
You'll also want to practice behavioral interviews like your offer depends on it because it does. And when he finally got another shot at IMC, one of the most elite firms out there, Aldo did things differently. He called up our cousin Fatima, who literally grilled him on behavioral interviews, until his answers sounded human. That paired with the technical grind helped him finally crack the code.
Now, when it pays, it pays big. And it paid off. IMC gave him a $20,000 signing bonus for an internship. Paid him over $100 an hour and set him up in a fully furnished apartment in downtown Chicago. That's like $50,000 of income in 10 weeks for an internship plus free luxury housing.
Now, let's talk about salary.
>> We'll give you a sign on bonus of $20,000. I >> was like, what? The sign on bonus is over 2x your entire summer from last year.
>> Yeah. Like what? Um, that was crazy, dude. My jaw dropped when I heard that. And then she's like, "And then your salary is going to be like I think it was like 12,500 a month, something like that." And then >> which is how much per hour roughly?
>> Maybe like 70 an hour or something.
>> How much is that? Per hour. Dude, I used to be really fast with this kind of stuff. No, I'm slow. We just hyped you up about math for like 40 minutes.
>> I think it's like 75 an hour. Is that >> Okay, fine. Yeah, roughly around 70 70 to 80.
>> Now I need to check.
>> All right, give him a quick second.
>> Okay, so $12,500 a month and you work 160 hours a month. 78.125.
>> Okay. Yeah. $78 an hour from $26 an hour to 78. So you three salary as a sophomore in college.
>> Yeah. And that's not even including the sign on bonus. If you include that sign on bonus.
>> Let me just include it just like to give a better estimate of >> what like the total hourly rate was. This is assuming you're working 40 hours a week. I was doing more like 50.
>> So overall, it's like $130 an hour if you factor in everything.
>> $130 an hour.
>> Yeah. And they pay for your housing, too, in Chicago. They pay for your housing. Downtown Chicago. They give you like 3K a month.
>> No, they they just give you a fully furnished great apartment in downtown Chicago. I didn't even know that was possible.
But don't get it twisted. This wasn't just free money and good vibes. Aldo was working 50 to 60 hour weeks diving deep into trading simulations, collaborating with top-tier engineers and traders, and picking up new skills fast. And what stood out the most, it wasn't about being the smartest person in the room. It was about being coachable, bringing value, and having decent social skills. That's what got him a return offer and eventually a $600,000 per year role the following summer. That's right, $600,000 a year out of college. Because while most people quit after bombing interviews, Aldo didn't. And that right there, that bounce-back energy is half the battle.
By now, you've probably realized getting a quant internship isn't about being a finance guy. It's about being technically sharp, knowing how to think, and proving it under pressure. Now, let's talk about the most slept-on strategy, which is communication. You could have the right answer, but if you sit there in silence and dump it at the end, they won't know how you got there. And in quant roles, your process matters as much as your answer. So, in order to win, you have to think out loud. Do this by practicing thinking out loud during mocks. Narrate your logic. Mention edge cases and admit what you're unsure about. Remember, it's not about being perfect. It's about showing how you problem-solve.
Now, you don't have to be a genius. Seriously, Aud says he was not the smartest in the room.
>> My experience is that you >> you do my experience is like you you absolutely need >> you have to be a genius.
>> And I I am a genius.
>> My experience is that you need a baseline >> Mhm. technical knowledge, but after that it's mostly how you work and how you interact with others if you desire to do a good job. That's that's been my experience.
What got him through wasn't just IQ. It was intentional prep, consistency, and curiosity. That's it. Most people think you need a 180 IQ for quant, but that's not true. You need solid fundamentals, consistent preparation, social skills, and a mindset that says, "I'll figure it out." So, if you're sitting there thinking, "I could never land a quant role." No, you just need to study the right way because when you learn the right things, luck has a funny way of showing up. And yes, the payoff is real. Again, let me just walk through all those stats. IMC Trading, $130 an hour, a $20k bonus, and a luxury apartment. A Jump Trading internship, $60,000 for 10 weeks, housing included. Now, these internships give you real ownership. You're not fetching coffee, you're shipping code, sitting on the trading floor, and making real impact. But you'll earn every cent. Although clocked in 50 to 60 hours a week. It's intense, but if you love solving hard problems, and if you want a world that pushes you, this is it.
And sure, not everyone's road looks the same. Just look at Sunny. People often ask, should I work at a startup, go corporate, or build my own thing? Sunny's answer is simple.
>> People who are trying to work on a startup should be uh looking at is taking advantage of all the different types of technologies that are out there. You'd be surprised that there are some technologies that you've probably never heard of that could probably solve problems that you have in your in your startup. So, it's not uh necessarily always about figuring out uh how do I create my own solution to solve this problem, but also figuring out how do I use different components that are already available so I'm not reinventing the wheel all the time. The other thing is I think it's very important for you to do market research on the types of startup that you're trying to create. Uh if you don't know your market and you don't know what types of competitors exist in your market and what features they have available already, um you will be first off wasting time and also your product won't be as valuable as you would want it to be. So I think that's a skill in itself that I didn't know how to do. And if I had to go back, I'd say, okay, you should probably learn how to do market research because market research is very important. It's not also only important for startups. It's important for like anything you want to.
Sunny didn't wait for senior year. He started doing real market research as a freshman. He joined URP, undergraduate research programs, and worked on wild projects, machine learning for climate and chemistry, computer vision, precision healthcare, and pathology. Even working under Ryan Tip Shirani. Yep. The son of Robert Tip Shirani, the guy who literally invented lasso regression. Now that's stats royalty, but Sunny didn't just stay academic. He also shipped real products, real startups. What was his rule?
>> They care about how you're transforming the technologies you're using and what impact it has on on the user base that you're trying to solve a problem for. Right? So, I think that these are all skills that are very necessary to have if you want to build a successful startup. You have to know how to do market research, know how to communicate your your findings and why they're important. Um, and then also just be able to communicate with people in general. That's also a very important skill. Uh, because at the end of the day, you need to sell your product uh for why it's important. And uh there's specific ways to go about that. Uh and it's a very hard skill to pick up. Um, but yeah, I'd say I'd say getting a focus on that and then practicing those would be very helpful for you.
If you're interested in building, build. Even if your first idea flops, you'll walk away with end-to-end experience that gives you leverage anywhere else. And look, this isn't about being perfect. It's about being relentlessly consistent. You don't need a 4.0 GPA. You don't need to be a genius. You just need a plan. And now you have one.
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