How to become a quant to data science reddit.

• How to become a quant to data science reddit I'm thinking about trying to switch from data science to quantitative research. These classes taught me what statistics is really like, and showed me all the parts of data analysis I missed in my first four years of taking AI classes only here. e. I had mathematics, statistics, machine learning, and a little computer science/programming, and I wanted a job where I could use all of that. Personally for trading I prefer data science students over statistics. It’s 100% more academic. Dead useful for a lot of quant work. You try setting beta to some low, nonzero value. They might ask for a general interest in finance, and why exactly did you apply or how did you get to know them, but that's about it, you just need to have an answer that's different from "I like the money". Companies typically take students who are already/have taken grad school classes more seriously. The work is somewhat research oriented. Only a few select firms like JSC recruit out of undergrad for Quant Research. Postings about current events are fine, as long as there is a political science angle. ) I am quite old (23), but would like to become a data scientist or a quant . Interesting. Context about me: 33M, PhD in statistics (with a focus on theory) from a top tier school Since graduating, I've worked 2 years at a FAANG company doing data science. #1 is my very first option and what I would like to do and #2 is more so of a backup. I don’t want to intimidate u/Expensive-Republic-2 with a huge list. But you'll also always be locked in as the quant. You (1) perform research on historical data to identify trading opportunities/ predictive signal, (2) back test your trading ideas by programming them into existence and then simulating them over some statistically significant period of time, and (3) you deploy them to a server that communicates with the exchange and I am an incoming MS student deciding between programs. Okay, the pro is my life horizon will be greatly expanded, where I could network with different types of either tech or non-tech elite or excellent ppl. Furthermore, you can get a data science job at a tech company, which is really competing with FAANG for work/pay. Dec 23, 2024 · The one advantage of quant stuff, as the work is very technical; the pay can scale very high. Yes, the interview process is especially brutal, since for some reason, you're basically required to be an expert in three disciplines (math, computer science, finance), and the positions tend be much sparser than say, a fundamental investment role on the sell side (as a strategist) or the buy side Dec 6, 2023 · Education: A quant typically holds an advanced degree (Master’s or Ph. at the end of the day modern quant trading is more into data science rather than the old school of complex maths. The field is asking for more education and PhDs are slowly becoming a necessary requirement versus just a preferred requirement. Quant research roles are primarily for advanced degrees like Masters and PhD’s. ) in a quantitative field such as Mathematics, Physics, Engineering, Computer Science, Financial Engineering, or Quantitative Finance. You don't need to take specific classes or a major to become a Quant Trader, you don't even need to know anything about finance. As in the quants were responsible for the ideas/theories for alpha generation, and the developers did all the programming. Another friend went tech -> qdev -> quant (in his 30s): had a math phd, went tech route first, was a bit of a mess/didn't build a good career so flipped to quant to start afresh. 5 years for undergrad is really quick, I bet you could crush a masters in a year with some planning. physics phds from good schools who want to become quants can do it just fine. Idk if i should major undergrad in data science or comp sci if i wanna become a data scientist. Don't bother with financial engineering or cs degrees. While you do your stats and math focused undergrad, get very very good at c++ and Python. I got my undergraduate in math and a masters in business and data analytics (switched from the actuary track). I have gone through: Skills needed to become a Quant; Types Feb 20, 2023 · I was hoping to get some insights about what steps I can take to break into Quantitative Finance as an MS Data Science student. I still appreciate the machine learning, data analysis, and advanced math and statistics components of the curriculum, but I'm considering if a more finance or pure mathematics-oriented A subreddit to discuss political science. We have a market data team but they handle things on a firm wide level, most desks have specific needs and that means at least one person has to be fairly involved with the data. As for tools that might be useful in quant and not data science, I can think of stochastic calculus, some advanced stats (e. Its going to come down to how much you are interested in the pure science with no relation to finance such as ms in CS, ms in data science, or MS in math / physics / stats. Where I am studying, quantitative specialization of business degree - Business Intelligence, Business Analytics, econometrics or Data science are all viable options even on job listings. As a computer science major, this path is sort of more clear and feasible. Nov 4, 2024 · Math + Stats would be an okay route to quant finance and a good back up if your interested in something like data science. I had to move into data science due to financial reasons. At the end of the day the only thing that matters is how much you know and how well you interview, if you get past the initial resume screen, an MS in data science is viewed as a stat + CS guy and their interview questions will revolve around those topics (more so in ML). To learn data science for a finance career, I recommend enrolling in courses at TutorT Academy. I'm okay to stay at NYC or jump to west coast. 2. No entry-level Quant job will ever require Finance experience, some firms will literally reject if you do have it. I was originally working as a space systems engineer designing satellite systems. 200 covers inference which is essential for any quant/data science work, you will need to know things like p-values and hypothesis testing and apply inference into case studies, 75% Masters isn't required for data science, but without relevant internship experience or a portfolio, it could be tough. You get the unit test running the appropriate data, and it does seem to be taking a long time to run. Data mining and deep learning would be extremely useful in data science but not so much in quant. Your math/stats skills matter much more than your communication and software engineering skills (assuming there’s are quant developers at the firm to implement strategies for you). , cython too if you can) with a solid command over probability and Specialize in quant and learn the basics of the data science field. This therefore puts a much greater premium on software engineering skills. I've met quant analysts in hedge funds and for one particular HF, the roles of the quant analysts and programmers were seperate. Quant will be great, but volatile. Jan 28, 2024 · In this article, I have shared tips and given the list of resources I’d use if I had to start over with becoming a Quant again. It's more niche, requires a physics PhD, paid way more than data scientist, less crowded than data science. Yes, you can pursue a data science career in finance. 100% agree that it’s more efficient to recruit from specific schools, my firm does not give a test to all applicants because there are a large amount of applicants and the number of people who pass the test is way more than the number of people we would actually recruit (this would make the actual in-person interviews way too competitive/selective and makes it harder for the interviewers as It wasn’t particularly difficult for me, depending on your definition of quant. in IB at risk management vs. So far my idea has been for a long time to study Data Science, but recently I've been reading about Quantitative Analysis, and I've started to become more interested on that. The areas of Quant Finance that I am most interested in are Quant Trading and Risk Analysis. Over the past couple of months, I have been researching quantitative finance, and I'm very interested! If I wanted to pivot into finance and become a Quant Researcher (particularly at a hedge fund), could I? Think it’ll be difficult for you to get a job in australia as an international student especially in quant finance. The thing about being a quant is the industry is competitive, so findings are not published like other industries. Start with QR and become a PM at a HF. I'm going to be finishing my Masters in Data Science this September and I’m interested in developing my skills towards a career as a Quantitative Analyst or Quant Trader. Jan 10, 2025 · Becoming a quant researcher is hypothetically possible, but you should be targeting a PhD for that instead. There are no other steps. Deep learning, for example. My initial interest in switching to a data analyst/data science/data career sort of revolved around sports analytics. So get data, acquire the technical + practical skills, and build quant models in your free time. (4. That’s actually how I started, as an analyst on an analyst team, before moving to data science at the same company after 1. But there's so few jobs, where they pay you so little, and who knows if you even have a voice in these organizations. What companies want is Data Science to deliver value and this means putting models in production to drive real impact. With the rise of AI, code generation, text based prompts, IMHO Both fields will be obsolete in 10 years. In my experience (2 actuarial internships + 3 passed exams and ~2 yrs work experience as a data scientist), actuaries are doing very specific math, while data scientists are more likely to use generalized tools. Another guy I vaguely knew, Brown grad, worked in data sci for a while seemed to have been doing pretty well then switched to a very top quant firm late 20s/early 30s. Id say doing masters of financial mathematics UNSW, masters of mathematical science USYD or ANU would be your best bet and changing your IT bachelors to computer science. but even without that should be no I'd also learn about and explore ways to over time and over several years improve upon your skills related to Computer Science, Statistics, Math, Data Science, Finance, and other relevant topics and fields of study. If a data scientist has an advanced degree in a related field, they may need to consider additional coursework or certifications in finance. Target, Starbucks offer education benefits you can use to complete courses and earn a degree. I have also realized that without any kind of domain knowledge, I am absolutely useless as a data scientist. This is where time series/GLM comes into play Sounds like the second choice is up your alley. Don’t bother with MATLAB, and probably not R. 39 votes, 14 comments. Most quantitative analyst have a PhD but a good percentage worked their way into the role. All of them have a masters. if you already have serious cs&coding under your belt and do the kind of physics that involves a lot of ML/big data/nontrivial statistics (I think some of the work with collider data or astrophysics is like that?) then you're likely to easily find very beneficial quant exits. For bioinformatics, you will certainly need a masters or PhD. This is assuming you want to focus more on quant and less data science. Read good quant books, papers and implement strategies. It's not unusual for top quants to have a PhD in math. To be a quant trader wasn’t massively difficult, to become a quant researcher was. Political science is the scientific study of politics. To become a quant developer you don't need to have done math heavy stuff as much as you'd need to be a very strong coder. I’m pretty familiar with the MIT MFin program (went there for undergrad) and have a close friend who went to Princeton MFin so can only speak on those. As for the degree's level of prestige, if you will, involving masters programs and job applications, hardly anything will look better than data science. Maintain a high gpa, do a lot of leetcode and greenbook, get an internship at FAANG, then get an internship at a quant firm. The one thing I worry about EU education is how much hands on education you get in a stats degree there. Then apply to internships. Quantitative finance is a lot like Data Science, but with more programming and CS skills. Prestigious, respected. Current total comp is ~270k. Allow yourself to be open to other pathways such as data science, finance, non quant trading, markets analysis etc have part time jobs that are relevant to building skills - programming, data analysis, math tutoring, customer service (for social skills) Tbh I would rather hire an analyst or have an analysts department, and train them up to do data science work so some of them can move into data science, than to create a junior data scientist position. Whilst Data Science seems more statistics, python, SQL. As for quant trading, landing a first interview is honestly not that hard like IB (However, the difficulty of the interview process is on a whole another level). Working as a "quant" in HFT vs. Don’t give up, quant is competitive. Some employers ex. However, pretty much all the information I've got about it comes from people in the US (also it feels everyone over at r/quant works in the US). For quant developer roles, a strong programmer (think competitive coding with C++ as main language but very good command over Python and relevant libraries too - pandas, numpy, scipy, vectorized coding, etc. majors extracurriculars and work experience? Is it possible to break into Quantitative finance if I learn from books or do employers want a certification of knowledge? It really depends on what you want to do as a quant. Preference: Master or more in Math, Statistics, Econometrics, Finance, (edge profile) Computer Science, (edge profile) Engineering Statistical arbitrage quant Statistical arbitrage quant, works on finding patterns in data to suggest automated trades. Incredibly difficult I imagine. This means there isn't any classes you can take that will teach you the more important bits. What matters is your course content and curriculum. Honestly, it doesn't really matter the major name. My career path so far has essentially been data scientist -> actuarial analyst -> quant trader -> quant research. . If I'm understanding correctly, it seems to be similar to the dynamic in the Data Science field. ) some quants also spend quite a bit of time on data management. ). So keep that in mind. I wanted to work on interesting problems and to use a wide variety of my skill set. Do all of this while having a 9-5 job related to quant (data analyst/ data science, software engineer etc). The techniques are quite different from those in derivatives pricing. As a quant with around 14yoe, I tend to agree and disagree with the some of the comments here. You won't be the negotiator. I have had interviews for quant positions and they are mostly brain teasers, IQ tests, the required knowledge is C++, stochastic calculus, algorithms. Get a bachelor's in stats and intern at quant shops every year and collect offers and network contacts. D. Let me add, each quant job requires a subset of this knowledge, rarely Will a job use it all. I am seeking entry level roles. The program trains you in Python, SQL, and R. or computer science. This is just my perspective based on what I'm seeing but Data Science seems to be becoming more of an engineering specialty as time goes by. You mess about for a while turning the data into a unit test - at first the data won't load, then you realise you were using the wrong loader configuration. The exposure of negotiations can lead to more interesting human work down the line. I’m expecting to graduate with a data science masters around December 2023. dont set your eyes on only quant - even the best get knocked back. true. For instance, I've heard many say that in order to be a good Data Scientist one needs to not only be good at the math/stats/programming, but to also have a strong domain knowledge about the field in which they work (pharma, finance, sales, etc. "Data science" has been a big buzzword the past few years and the field is only going to exponentiate throughout the decade. Algorithmic trading is programming and research. A minor in Computer Science or Business Analytics would complement the major well. 5 years. These are a few things that personally attracted me to this career: Quant trading is a career that you may be interested in if you are a very quantitative, academic person. data Not quant in general just Jane Street/Citadel/etc. Mar 4, 2025 · A more typical career path is starting out as a data research analyst and becoming a quant after a few years. The master's in data science vs master's in CS is a stupid debate that people have on here because a lot of people feel threatened or feel territorial. If you aren't at a top school or if you aren't exceptional (or very lucky) getting a quant job right out the gate will be hard, so you first need to show that you are actually good on paper. For my dream job, I definitely would prefer quantitative-heavy positions such as machine learning engineer or quantitative analyst as opposed to BI developer or data engineer. People don't want to hear this, but you just have to be smart, and have good intuition for probability, game theory, and maybe some mental math. In Europe, it seems to me That confidence will only come from knowing you can be a good quant. I wanted to be a quant developer but since I couldn't opt for physics I chose to become a data scientist. That being said, MFE grads have an opening for quant trading roles in the following ways: Starting out at firms where quant trading is effectively quant trading+quant research, and transitioning to a pure quant trading kind of role at a different firm. Working in quantitative finance, as a quant analyst, quant dev, quant researcher, or trader Working anywhere besides quant finance, as a data scientist. I don’t do any of this though, and it’s more of a peripheral responsibility. g. copulae), and optimization techniques. I have seen a lot of people who became data analysts with business degree, since for most positions its enough to know stats until regressions, R+SQL. Odds are, you won’t get any solid bites for a quant internship this summer. P World - Using data science to uncover signals. I'm on track to complete my bachelor's in mechanical engineering with a minor in math and CS (from a non-target US school). I would say no, an actuary can't do the job of a data scientist and a data scientist could not do the job of an actuary (without training). Eliminate factors such as institutional prestige, cost or alumni network, and simply look at statistics vs. I interned in quant research for a bit. I also wasn’t deliberately making the transition. CDOs are completely different disciplines. Your degree will only get you the interview. For a career in quant or data science, a major in either Finance or Economics (with a focus on data analysis or mathematical economics) would be beneficial. Over the past year, my interests have shifted away from the pure computer science aspects of Data Science, and I'm drawn to the prospect of becoming a quant. While I do like ML, I hate anything to do with images, videos or text data. Data science is increasingly being used in the finance industry for tasks such as risk management, fraud detection, algorithmic trading, and customer analytics. Hey everyone, I’m (33) currently a quantitative analyst on track to become a data scientist. Anyway, to each their own. MIT’s MFin program can be quantitative as MFEs if you want to (just half the people don’t want to be quants). I have experience as a part-time Data Scientist at a software development company and have an opportunity available to work as a data scientist at a start-up bank when I Hi guys I'm a finance accounting major with no experience in Maths only at a high school level, wanting to become a Quant one day, what's the typical pathway for a Quant? I. Below are some details about my background. Dive deep into finance industry, and try to become quant. For example some quants will be very focussed on their economic area and barely even touch regression and their knowledge comes from actual trading knowledge. social media sentiment traders outperformed old-school traders in the last few years. I’m following the path that other quantitative analyst (who only have a masters degree) have taken. Data science will be more stable. Develop Your Technical Skills . Data science just wasn’t cutting it, so I interviewed and got an offer. Step 2. You are confusing Quant Trader with a Quant Researcher. Quant trading requires a large set of skills within various disciplines, drawing most from Statistics, Math, Computer Science, Data Science, and Machine Learning. Quant work is like being a surgeon with numbers. just look into the quant positions job feeds, most of the requirements are similar to a data scientist specialized in finance. Some data science could help too. (I will also have a second masters degree — masters in library and information science, not sure how relevant that is. Had I been in your place, I would have tried for quant developer role. Probably want to take some math courses, specifically probability and statistics. It deals with systems of governance and power, and the analysis of political activities, political thought, political behavior, and associated constitutions and laws. If you want to become a really top level quant, like the ones who get paid a shitton of money, some amount of graduate school is probably required. vprgj kfijor qwh mfra vgcmff sqdop qgwjh veumi fiop aksy zlsq qfjbehx qdbgmz wzuruz zrk