Data scientist vs quant researcher reddit But I heard it's not that simple to get into quantitative researcher (and other quant positions) for just Physics PhDs anymore. 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 Personally for trading I prefer data science students over statistics. And they’re both ranked well. a good data science program could be better for breaking into quant than a lower ranked MFE program. Physics geek here, who's worked in data science. Working as a "quant" in HFT vs. becoming a quant, especially on the buyside, provides you with the opportunity to advance to senior quant, sub pm, portfolio manager, or even to run your own fund one day, or just retire a It wasn’t particularly difficult for me, depending on your definition of quant. Likewise, if you want to do research based work (quant researcher and quant software engineer are the two primary roles you'll probably be interested in) then a phd is specializing in research and learning all of that, so I’m SS quant researcher (or strategist as some banks call it) at a BB a couple things stand out: we sit in research (Equity Research) we publish our alpha models/research/apt data sets for buy side clients to use. However, I’m not sure as to which subject I should pursue a PhD in. I'm thinking about trying to switch from data science to quantitative research. My career path so far has essentially been data scientist -> actuarial analyst -> quant trader -> quant research. Your degree will only get you the interview. I’ll second everyone here and recommend Python. ) of being a quant over data science in your opinion? Is it relatively easy for a person with quant skillsets to take on a job as a data scientist/data analyst with some side project experiences or MOOCs? Apr 23, 2025 · Those working in the field are quantitative analysts (quants). Here in the equity alpha industry, there are several job types: data scientists, quantitative researchers, portfolio managers, and traders. I am a freshly graduate student from a tier-3 university ( In India) with a Computer Science Engineering degree and I got placed at a start-up (now MNC) with a Data Scientist role ( Although my job will start from Jan, they delayed it citing the recession, it was a startup when I got the job but between the time aquisition happened and it got under an MNC) Some people call any software work at a trading firm 'quant,' while others mean specifically portfolio management/trading/research scientist (this is where the real money is, and it's a totally different ladder than generic software engineering). Hi people, I am currently working as a software engineer in FAANG, and am contemplating moving to the quant research careers in the trading industry via a Masters in Financial Engineering / Computational Finance. A lot of companies muddle the difference between the two, and some companies (esp FAANG) actually removed the term "Data Analyst" and replaced it with "Data Scientist". I feel like for quant research, you need much more math than typical data scientist to be successful though. Yeah this is really crucial difference. I am a bit of confused whether I should pursue Data Scientist or Quantitative Analyst as my future career plan. I've been trying to get into the quant industry (espc. But it’s mainly just cause data science jobs are literal dog shit and don’t actually translate to what “science” is with data. MM firms are quite different to quant hedge funds like DE Shaw or Two Sigma but still pay obscene amounts and the overall goal is still to make as much money as possible. I’m a execution trader at a quant HF where the researchers are the ones generating signals and therefore eligible for P&L sharing and the highest compensation. Work environment and peers. Also keep in mind, most quant finance and data science classes start as a 4th year class or as a 1st year masters class. in IB at risk management vs. Not an existence argument. I get you read black swan once but trust me, I work in data science and have been in the space since 2008. 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. However since I came from an analytics background, I'm always interested in mathematics and machine learning. cross functional teams, embedded data scientist, data science team) What kind of projects have you worked on What is the scope of those projects (end-to-end, workshops, short projects). Exit opps I use means, sums, sometimes rolling, bucketing by quantiles, maybe linear regression once a month or so, maybe tuning some out of the box optimizer once a year or so. Depends on where you are (e. ). Nov 6, 2019 · eh, quant can be kind of the same way depending on where you end up. Other common segments, such as pricing, structuring, and trading FICC products, broadly fall under the quant definition. You are looking for data science position which are not the same. I’ve always liked math and statistics especially and have been thinking about graduate school first, but long term I don’t think I won’t to go back to an industry data science job, but rather I want to break into quant research or trading. Mar 9, 2020 · What’s certain is that the quantitative analyst vs. Citadel made 28B gross last year, and returned investors 16B net of fees. On the trading desk, we manage risk intraday and exercise some level of discretion in semi-systematic books, s Not the headquarters but still has a few hundred employees and a very big quant team. Of course one shouldn't read it as "data science BAD" without any qualifiers, or that "data science-like quant" is bad. ) in a quantitative field such as Mathematics, Physics, Engineering, Computer Science, Financial Engineering, or Quantitative Finance. Now based on my current work experience, I have two major future career development: Quant Researcher vs Data Scientist I've been fortunate enough to land internships at some of my dream companies for the upcoming summer. 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. That said the most popular for this stuff is python from what I’ve seen in job listings and talking to other AMs. Oct 16, 2012 · I currently work in data science/machine learning at a tech unicorn, so have tried both the tech data science and finance jobs. Data scientists can be in similar roles, but some data scientists are more business focused. Please do tell us how quant finance stuff "is of another scale" to data science at tech companies with 100's of million to billions of users. Did real analysis undergrad for mathematicians and it's way too theory focused for a dummy like me. I was a trader but also worked very closely with the quant team. CFO), whereas Data Science would peak at something like a chief of insights/analytics for a company. Dec 6, 2023 · Education: A quant typically holds an advanced degree (Master’s or Ph. Quants tend to specialize in specific areas which may include derivative structuring or pricing, risk management, algorithmic trading and investment management. The skillset isn't straightforward swap. A minor in Computer Science or Business Analytics would complement the major well. It really depends on what you want to do as a quant. It's a buzzword. In your situation, it’s the best bang for your buck: 1) you are new to programming and Python is a good first language, 2) Python has a lot of libraries for data science and machine learning, and 3) Python is widely used in quant research. I was formerly a data scientist at a large company and am currently a quant researcher at a hedge fund, so I have some insight about this. In this article, we compare quantitative analyst vs. Work life balance. Creating values with quantitative methods then you’re in In terms of preparing for a generic role as a quant. Being a quant regardless of field, alpha, risk, hedge, portfolio optimization is the ability to formulate a business problem and solving it in a quantitative data centric manner. At any given time, there are 10x more available data scientist jobs than quant finance jobs, and the data scientist jobs are far better. Quantitative research is real data science to me The research unit of my previous degree (quant business) was called decision science and had applied stat, operations research (OR), data science and information systems engineering under it. If I'm understanding correctly, it seems to be similar to the dynamic in the Data Science field. elementary probability and stats Even though the quant finance stuff might be "data science", it is of another scale entirely, such that the terminology is completely different in another class. 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. I have never worked in a quant fund and I don't know anybody who has, so I have no idea. black-scholes formula has some special functions in it I guess, and maybe on occasion I do derive something algebraically on paper or in sympy, with some algebra and derivatives, but not often. data scientist question is one that provokes significant online debate. I honestly wouldn’t recommend anything reading wise. The big upside for Rutgers is that it’s in person and I’ll be able to do research, which I really wanna do. Sounds like the author might not have realized this upfront. Current total comp is ~270k. 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. "Data science" has been a big buzzword the past few years and the field is only going to exponentiate throughout the decade. financial analyst is different from a BI analyst, etc. The data science team at my firm (quant hedge fund) focuses on data platforms, data engineering, sourcing data, and processing data, all in collaboration with the quant research teams who use the data to actually do their research and come up with or refine strategies. Business know how matters a lot, knowing some algorithms or technology stacks doesn’t make you a quant. To answer you question, is there jobs inherently similar? Career path: Quant vs Data scientist. The you can easily apply that in quant fi or data Sci. g. It might be the case for a data scientist to earn a DS matter. I would say take numerical methods in python. Your background is perfect, quant firms specifically looks for math/stats graduate, but PHD is usually preferred for a quant research role. Dec 23, 2024 · A master’s in econometrics/quantitative finance or financial engineering are probably the most common degrees for quant researchers, but you’ll see plenty of people with maths, stats, physics, engineering, computer science backgrounds too (as long as it involves heavy maths) Nov 6, 2019 · What are the advantages (stability, pay, employment opportunity, etc. Now, hee's the general rule about financial firms: the closer you are to the money, the more you make. I am quite old (23), but would like to become a data scientist or a quant . To be a quant trader wasn’t massively difficult, to become a quant researcher was. Only a few select firms like JSC recruit out of undergrad for Quant Research. I know very little about GT wrt quant but what I have heard is quite positive, so I’ll leave that to you to research. In many ways the jobs are more similar than I thought. C/C++ is amazing and fast. Skill sets between data science and quant finance do overlap, but there are also differences, like C++ & stochastic calculus for certain areas in quant finance. If you're a quant dev, implementing those models, you make less. Please help me by comparing the two lines, I need a few data points. 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% Salary will be higher on the Data Science side for sure, especially starting out. data scientist by looking at what they do, how they’re trained, what they work on, and how well they’re paid. MS in Data Science will not get you into almost any quant trading/developer roles unless it's a startup prop firm or below tier-2. 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. It's a frequency argument. 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. Nothing goes to our traders. As others have mentioned quant researcher is a more statistically advanced role and does need masters + research experience or a PHD. hedge and prop firms) and I can give you some insights i gained. quant traders are usually king in market makers, while quant researchers and quant devs are sidelined a bit. 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). , would help. Though I can see Finance leading to very senior and executive positions in a company (e. They don't care if you don't know a single bit of finance. OP is asking about ML researcher which is either a research scientist position or a ML Engineer position at Meta. Also good to keep in mind that working as a researcher for example will likely require advanced degrees, whether that be a phd or masters so your choice of undergrad isn’t absolutely critical… "Quant" is a basically meaningless term at this point, not unlike "AI". ) and Data Science (Either in tech industry or finance or F500), which would be a lucrative option - In terms of - Compensation (Base+Bonus). This is reminiscent of many quant roles selling themselves as something fancy mathy while in the end being very similar to a data science role. I've narrowed it down to the two listed below. but yes data science typically means people who can do all that analysts can do I see what you're getting at, but phrased this way it's incorrect. I know most data science programs tend to be “cash cows” or too watered down but I did extensive research into their curriculums and they’re both pretty rigorous. In both my quant group and DS group, I collect data, build models using statistics and machine learning, and write production software. I have a degree in Math and am pretty good with python already so I can grasp the ideas pretty readily. D. What is your work mode (e. So my question is if I'm a Physics PhD from a top uni with programming experience (I've taken lot of CS courses) can I still get into a good quantitative researcher role (not some role which has mostly undergrads anyway). I would focus less on job title-based career progression and focus more on what their respective roles entail and whether they meet your expectations and wants. I've been taking this course online and the course is supposed to be a beginners introduction to Data Science and Neural Networks. ), but product analysts often have product intuition and domain knowledge that data scientists typically don't. Preference: Math, Statistics, Operational research, computer science, (edge profile) Engineering Capital Quant A capital quant works on modelling the bank’s credit exposures and capital requirements. I have been working as quant researcher for about 3 years at one of the top 20 hedge funds in US (not quant hedge fund). . Traders at banks can’t take discretionary risks so can’t really use our alpha models. And it's extremely unlikely to go directly from undergrad to a IC3 in DS for a FAANG, unless you had internships at those places and are a top university. if you're going into quant for the exit opportunities you're probably doing it for the wrong reasons. Quant research roles are primarily for advanced degrees like Masters and PhD’s. Putting the brand names aside, I want to know which field has a better long-term situation, I have heard people talking about DS going downward as AI blooms and Quant has higher salaries (maybe these infos are not accurate). Others reference highly specialized infrastructure work. This is perfect for quant developer and quant trader roles. We’ll cover: Rule of thumb is higher risk / higher reward based on how close you are to alpha generation and monetization. It also helps to give a ballpark of their usual timeframe What are your responsibilities in those projects 2012 - Lead UX Researcher at consultancy: $116k salary 2016 - Sr UX Researcher at a MAANG: $160 base + $25k RSUs per year + $50k signing bonus 2021 - Promoted to Principal UX Researcher: $197k base + $150k RSUs per year + $50k bonus 2023 - Joined a startup as Principal UX Researcher: $215k salary When I complete my degree I may consider applying to quant roles but at that point my interests may change and I may go into some other industry. The whole idea that quant is the most intellectually stimulating role in the world, is also bogus, from the standpoint that I’ve also talked to real data scientists, (who this subreddit likes to clown repeatedly), where their work is actually data science, and they do more modeling than some of the quants I’ve talked to. Classical "Data Scientist" has now become "Applied Scientist" or "Research Scientist" or even "ML Engineer" in some companies. Mar 9, 2020 · In every Reddit or Quora thread about the difference between quantitative analysts and data scientists, some commenters argue that where someone works determines whether they’re a quant or a data scientist. 20% of Citadel's investors are employees, possibly more (and the amount invested in the fund grows disproportionately with seniority/role), so in total citadel staff and board made 12B fees + 20% of 16B ≈ 15B. I have had interviews for quant positions and they are mostly brain teasers, IQ tests, the required knowledge is C++, stochastic calculus, algorithms. Statistics appears to be the most common one on LinkedIn at top firms and hedge funds, however, my concern is that then my skillset would be limited to statistical modeling. Hi I'm now working at a fintech in NYC as software engineer. For quant development, MS CS in tier-1 schools with great scores in competitive coding programs, participation/trophies from ACM ICPC type tournaments, etc. Yeah, a bit. The work is somewhat research oriented. So if you're a quant researcher, coming up with models, you make a lot. Obviously if you have an offer to go and be say a quant on the pricing team at an options firm, there’s a bunch of stuff you should go and look at I call them the data scientist and analyst, before the term was coined, it is essentially portfolio optimization and inefficiency finder. for most people, quant IS the end goal, the dream job. I also wasn’t deliberately making the transition. 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). CDOs are completely different disciplines. My 2c at least. It’s super varied, every firm has their own flavour on the role and on the kinds of models, techniques and assumption that are in play. If a data scientist has an advanced degree in a related field, they may need to consider additional coursework or certifications in finance. 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. as for OP’s question it depends on the relative brand name of the two programs. All these domains are focused on optimising (business) decisions in similar but very distinct ways. Quant Research/Data Science Salary at hedge fund I am 27M with MFE from top US program - think Baruch, Columbia etc. Among Investment banking, Sales and Trading, Quantitative Finance (Quantitative trading and quantitative research, quantitative development etc. 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. We would like to show you a description here but the site won’t allow us. Each firm draws the line a little bit differently between QR and QT, but traders are generally king. 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). Whilst Data Science seems more statistics, python, SQL. I wanna do a PhD and then one of the careers that I’m aiming for is in quant finance research. quant is a lot more specialized so u can get pigeonholed and if ur specialization is no longer a hot sub field, then ur kind of SOL. I don't personally believe that "data science ML methods will eventually replace Operations Research methods" because they are differing things. Quant researchers are very much so just pure math or stat phd holders who take their academic research to the real world and apply it to finance. However, these individuals are not data scientists, they're operations research analysts with development ability (not on par with a software engineer). gzlfiblkeesdwwscwjlbjiewcxooxyjoxwfucauxrodcmvzpuosydbvyegfpkqwzpahzwmelwyj
Data scientist vs quant researcher reddit But I heard it's not that simple to get into quantitative researcher (and other quant positions) for just Physics PhDs anymore. 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 Personally for trading I prefer data science students over statistics. And they’re both ranked well. a good data science program could be better for breaking into quant than a lower ranked MFE program. Physics geek here, who's worked in data science. Working as a "quant" in HFT vs. becoming a quant, especially on the buyside, provides you with the opportunity to advance to senior quant, sub pm, portfolio manager, or even to run your own fund one day, or just retire a It wasn’t particularly difficult for me, depending on your definition of quant. Likewise, if you want to do research based work (quant researcher and quant software engineer are the two primary roles you'll probably be interested in) then a phd is specializing in research and learning all of that, so I’m SS quant researcher (or strategist as some banks call it) at a BB a couple things stand out: we sit in research (Equity Research) we publish our alpha models/research/apt data sets for buy side clients to use. However, I’m not sure as to which subject I should pursue a PhD in. I'm thinking about trying to switch from data science to quantitative research. My career path so far has essentially been data scientist -> actuarial analyst -> quant trader -> quant research. Your degree will only get you the interview. I’ll second everyone here and recommend Python. ) of being a quant over data science in your opinion? Is it relatively easy for a person with quant skillsets to take on a job as a data scientist/data analyst with some side project experiences or MOOCs? Apr 23, 2025 · Those working in the field are quantitative analysts (quants). Here in the equity alpha industry, there are several job types: data scientists, quantitative researchers, portfolio managers, and traders. I am a freshly graduate student from a tier-3 university ( In India) with a Computer Science Engineering degree and I got placed at a start-up (now MNC) with a Data Scientist role ( Although my job will start from Jan, they delayed it citing the recession, it was a startup when I got the job but between the time aquisition happened and it got under an MNC) Some people call any software work at a trading firm 'quant,' while others mean specifically portfolio management/trading/research scientist (this is where the real money is, and it's a totally different ladder than generic software engineering). Hi people, I am currently working as a software engineer in FAANG, and am contemplating moving to the quant research careers in the trading industry via a Masters in Financial Engineering / Computational Finance. A lot of companies muddle the difference between the two, and some companies (esp FAANG) actually removed the term "Data Analyst" and replaced it with "Data Scientist". I feel like for quant research, you need much more math than typical data scientist to be successful though. Yeah this is really crucial difference. I am a bit of confused whether I should pursue Data Scientist or Quantitative Analyst as my future career plan. I've been trying to get into the quant industry (espc. But it’s mainly just cause data science jobs are literal dog shit and don’t actually translate to what “science” is with data. MM firms are quite different to quant hedge funds like DE Shaw or Two Sigma but still pay obscene amounts and the overall goal is still to make as much money as possible. I’m a execution trader at a quant HF where the researchers are the ones generating signals and therefore eligible for P&L sharing and the highest compensation. Work environment and peers. Also keep in mind, most quant finance and data science classes start as a 4th year class or as a 1st year masters class. in IB at risk management vs. Not an existence argument. I get you read black swan once but trust me, I work in data science and have been in the space since 2008. 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. However since I came from an analytics background, I'm always interested in mathematics and machine learning. cross functional teams, embedded data scientist, data science team) What kind of projects have you worked on What is the scope of those projects (end-to-end, workshops, short projects). Exit opps I use means, sums, sometimes rolling, bucketing by quantiles, maybe linear regression once a month or so, maybe tuning some out of the box optimizer once a year or so. Depends on where you are (e. ). Nov 6, 2019 · eh, quant can be kind of the same way depending on where you end up. Other common segments, such as pricing, structuring, and trading FICC products, broadly fall under the quant definition. You are looking for data science position which are not the same. I’ve always liked math and statistics especially and have been thinking about graduate school first, but long term I don’t think I won’t to go back to an industry data science job, but rather I want to break into quant research or trading. Mar 9, 2020 · What’s certain is that the quantitative analyst vs. Citadel made 28B gross last year, and returned investors 16B net of fees. On the trading desk, we manage risk intraday and exercise some level of discretion in semi-systematic books, s Not the headquarters but still has a few hundred employees and a very big quant team. Of course one shouldn't read it as "data science BAD" without any qualifiers, or that "data science-like quant" is bad. ) in a quantitative field such as Mathematics, Physics, Engineering, Computer Science, Financial Engineering, or Quantitative Finance. Now based on my current work experience, I have two major future career development: Quant Researcher vs Data Scientist I've been fortunate enough to land internships at some of my dream companies for the upcoming summer. 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. That said the most popular for this stuff is python from what I’ve seen in job listings and talking to other AMs. Oct 16, 2012 · I currently work in data science/machine learning at a tech unicorn, so have tried both the tech data science and finance jobs. Data scientists can be in similar roles, but some data scientists are more business focused. Please do tell us how quant finance stuff "is of another scale" to data science at tech companies with 100's of million to billions of users. Did real analysis undergrad for mathematicians and it's way too theory focused for a dummy like me. I was a trader but also worked very closely with the quant team. CFO), whereas Data Science would peak at something like a chief of insights/analytics for a company. Dec 6, 2023 · Education: A quant typically holds an advanced degree (Master’s or Ph. Quants tend to specialize in specific areas which may include derivative structuring or pricing, risk management, algorithmic trading and investment management. The skillset isn't straightforward swap. A minor in Computer Science or Business Analytics would complement the major well. It really depends on what you want to do as a quant. It's a buzzword. In your situation, it’s the best bang for your buck: 1) you are new to programming and Python is a good first language, 2) Python has a lot of libraries for data science and machine learning, and 3) Python is widely used in quant research. I was formerly a data scientist at a large company and am currently a quant researcher at a hedge fund, so I have some insight about this. In this article, we compare quantitative analyst vs. Work life balance. Creating values with quantitative methods then you’re in In terms of preparing for a generic role as a quant. Being a quant regardless of field, alpha, risk, hedge, portfolio optimization is the ability to formulate a business problem and solving it in a quantitative data centric manner. At any given time, there are 10x more available data scientist jobs than quant finance jobs, and the data scientist jobs are far better. Quantitative research is real data science to me The research unit of my previous degree (quant business) was called decision science and had applied stat, operations research (OR), data science and information systems engineering under it. If I'm understanding correctly, it seems to be similar to the dynamic in the Data Science field. elementary probability and stats Even though the quant finance stuff might be "data science", it is of another scale entirely, such that the terminology is completely different in another class. 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. I have never worked in a quant fund and I don't know anybody who has, so I have no idea. black-scholes formula has some special functions in it I guess, and maybe on occasion I do derive something algebraically on paper or in sympy, with some algebra and derivatives, but not often. data scientist question is one that provokes significant online debate. I honestly wouldn’t recommend anything reading wise. The big upside for Rutgers is that it’s in person and I’ll be able to do research, which I really wanna do. Sounds like the author might not have realized this upfront. Current total comp is ~270k. 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. "Data science" has been a big buzzword the past few years and the field is only going to exponentiate throughout the decade. financial analyst is different from a BI analyst, etc. The data science team at my firm (quant hedge fund) focuses on data platforms, data engineering, sourcing data, and processing data, all in collaboration with the quant research teams who use the data to actually do their research and come up with or refine strategies. Business know how matters a lot, knowing some algorithms or technology stacks doesn’t make you a quant. To answer you question, is there jobs inherently similar? Career path: Quant vs Data scientist. The you can easily apply that in quant fi or data Sci. g. It might be the case for a data scientist to earn a DS matter. I would say take numerical methods in python. Your background is perfect, quant firms specifically looks for math/stats graduate, but PHD is usually preferred for a quant research role. Dec 23, 2024 · A master’s in econometrics/quantitative finance or financial engineering are probably the most common degrees for quant researchers, but you’ll see plenty of people with maths, stats, physics, engineering, computer science backgrounds too (as long as it involves heavy maths) Nov 6, 2019 · What are the advantages (stability, pay, employment opportunity, etc. Now, hee's the general rule about financial firms: the closer you are to the money, the more you make. I am quite old (23), but would like to become a data scientist or a quant . To be a quant trader wasn’t massively difficult, to become a quant researcher was. Only a few select firms like JSC recruit out of undergrad for Quant Research. I know very little about GT wrt quant but what I have heard is quite positive, so I’ll leave that to you to research. In many ways the jobs are more similar than I thought. C/C++ is amazing and fast. Skill sets between data science and quant finance do overlap, but there are also differences, like C++ & stochastic calculus for certain areas in quant finance. If you're a quant dev, implementing those models, you make less. Please help me by comparing the two lines, I need a few data points. 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% Salary will be higher on the Data Science side for sure, especially starting out. data scientist by looking at what they do, how they’re trained, what they work on, and how well they’re paid. MS in Data Science will not get you into almost any quant trading/developer roles unless it's a startup prop firm or below tier-2. 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. It's a frequency argument. 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. Nothing goes to our traders. As others have mentioned quant researcher is a more statistically advanced role and does need masters + research experience or a PHD. hedge and prop firms) and I can give you some insights i gained. quant traders are usually king in market makers, while quant researchers and quant devs are sidelined a bit. 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). , would help. Though I can see Finance leading to very senior and executive positions in a company (e. They don't care if you don't know a single bit of finance. OP is asking about ML researcher which is either a research scientist position or a ML Engineer position at Meta. Also good to keep in mind that working as a researcher for example will likely require advanced degrees, whether that be a phd or masters so your choice of undergrad isn’t absolutely critical… "Quant" is a basically meaningless term at this point, not unlike "AI". ) and Data Science (Either in tech industry or finance or F500), which would be a lucrative option - In terms of - Compensation (Base+Bonus). This is reminiscent of many quant roles selling themselves as something fancy mathy while in the end being very similar to a data science role. I've narrowed it down to the two listed below. but yes data science typically means people who can do all that analysts can do I see what you're getting at, but phrased this way it's incorrect. I know most data science programs tend to be “cash cows” or too watered down but I did extensive research into their curriculums and they’re both pretty rigorous. In both my quant group and DS group, I collect data, build models using statistics and machine learning, and write production software. I have a degree in Math and am pretty good with python already so I can grasp the ideas pretty readily. D. What is your work mode (e. So my question is if I'm a Physics PhD from a top uni with programming experience (I've taken lot of CS courses) can I still get into a good quantitative researcher role (not some role which has mostly undergrads anyway). I would focus less on job title-based career progression and focus more on what their respective roles entail and whether they meet your expectations and wants. I've been taking this course online and the course is supposed to be a beginners introduction to Data Science and Neural Networks. ), but product analysts often have product intuition and domain knowledge that data scientists typically don't. Preference: Math, Statistics, Operational research, computer science, (edge profile) Engineering Capital Quant A capital quant works on modelling the bank’s credit exposures and capital requirements. I have been working as quant researcher for about 3 years at one of the top 20 hedge funds in US (not quant hedge fund). . Traders at banks can’t take discretionary risks so can’t really use our alpha models. And it's extremely unlikely to go directly from undergrad to a IC3 in DS for a FAANG, unless you had internships at those places and are a top university. if you're going into quant for the exit opportunities you're probably doing it for the wrong reasons. Quant research roles are primarily for advanced degrees like Masters and PhD’s. Putting the brand names aside, I want to know which field has a better long-term situation, I have heard people talking about DS going downward as AI blooms and Quant has higher salaries (maybe these infos are not accurate). Others reference highly specialized infrastructure work. This is perfect for quant developer and quant trader roles. We’ll cover: Rule of thumb is higher risk / higher reward based on how close you are to alpha generation and monetization. It also helps to give a ballpark of their usual timeframe What are your responsibilities in those projects 2012 - Lead UX Researcher at consultancy: $116k salary 2016 - Sr UX Researcher at a MAANG: $160 base + $25k RSUs per year + $50k signing bonus 2021 - Promoted to Principal UX Researcher: $197k base + $150k RSUs per year + $50k bonus 2023 - Joined a startup as Principal UX Researcher: $215k salary When I complete my degree I may consider applying to quant roles but at that point my interests may change and I may go into some other industry. The whole idea that quant is the most intellectually stimulating role in the world, is also bogus, from the standpoint that I’ve also talked to real data scientists, (who this subreddit likes to clown repeatedly), where their work is actually data science, and they do more modeling than some of the quants I’ve talked to. Classical "Data Scientist" has now become "Applied Scientist" or "Research Scientist" or even "ML Engineer" in some companies. Mar 9, 2020 · In every Reddit or Quora thread about the difference between quantitative analysts and data scientists, some commenters argue that where someone works determines whether they’re a quant or a data scientist. 20% of Citadel's investors are employees, possibly more (and the amount invested in the fund grows disproportionately with seniority/role), so in total citadel staff and board made 12B fees + 20% of 16B ≈ 15B. I have had interviews for quant positions and they are mostly brain teasers, IQ tests, the required knowledge is C++, stochastic calculus, algorithms. Statistics appears to be the most common one on LinkedIn at top firms and hedge funds, however, my concern is that then my skillset would be limited to statistical modeling. Hi I'm now working at a fintech in NYC as software engineer. For quant development, MS CS in tier-1 schools with great scores in competitive coding programs, participation/trophies from ACM ICPC type tournaments, etc. Yeah, a bit. The work is somewhat research oriented. So if you're a quant researcher, coming up with models, you make a lot. Obviously if you have an offer to go and be say a quant on the pricing team at an options firm, there’s a bunch of stuff you should go and look at I call them the data scientist and analyst, before the term was coined, it is essentially portfolio optimization and inefficiency finder. for most people, quant IS the end goal, the dream job. I also wasn’t deliberately making the transition. 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). CDOs are completely different disciplines. My 2c at least. It’s super varied, every firm has their own flavour on the role and on the kinds of models, techniques and assumption that are in play. If a data scientist has an advanced degree in a related field, they may need to consider additional coursework or certifications in finance. 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. as for OP’s question it depends on the relative brand name of the two programs. All these domains are focused on optimising (business) decisions in similar but very distinct ways. Quant Research/Data Science Salary at hedge fund I am 27M with MFE from top US program - think Baruch, Columbia etc. Among Investment banking, Sales and Trading, Quantitative Finance (Quantitative trading and quantitative research, quantitative development etc. 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. We would like to show you a description here but the site won’t allow us. Each firm draws the line a little bit differently between QR and QT, but traders are generally king. 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). Whilst Data Science seems more statistics, python, SQL. I wanna do a PhD and then one of the careers that I’m aiming for is in quant finance research. quant is a lot more specialized so u can get pigeonholed and if ur specialization is no longer a hot sub field, then ur kind of SOL. I don't personally believe that "data science ML methods will eventually replace Operations Research methods" because they are differing things. Quant researchers are very much so just pure math or stat phd holders who take their academic research to the real world and apply it to finance. However, these individuals are not data scientists, they're operations research analysts with development ability (not on par with a software engineer). gzlfibl keesd wwscw jlbjiew cxoo xyjo xwfuc auxr odcmv zpuos ydbv yegfp kqwzp ahzwme lwyj