Data scientist vs quant researcher reddit You won't be the negotiator. My initial interest in switching to a data analyst/data science/data career sort of revolved around sports analytics. I think Facebook did this first and a lot of other tech orgs followed suit to compete for talent. It can also be someone who does actual statistics, NLP, regression, neural networks, etc. I honestly wouldn’t recommend anything reading wise. All quant colleagues I've had are either right out of undergrad, post phd or MFE. in terms of coding skill you can generally work with CSCareerQuestions protests in solidarity with the developers who made third party reddit apps. The you can easily apply that in quant fi or data Sci. What distinguishes a great data scientist from a decent one is the ability to solve the right problem in a sensible way. But there's so few jobs, where they pay you so little, and who knows if you even have a voice in these organizations. 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. The sign of a company having mature Data Science team is that whether it has separated the role of a data scientist from that of a data engineer. but yes Mar 9, 2020 · The reality is that no one is winning the quantitative analyst vs. 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. This also includes ML models like PCA as well as other models like HMM. I've been trying to get into the quant industry (espc. Problem-Solving Skills : Math trains you to tackle complex problems and think abstractly, skills highly valued in top tech companies and research institutions. Mar 9, 2020 · What’s certain is that the quantitative analyst vs. What is your work mode (e. Personally for trading I prefer data science students over statistics. Ultimately, finding the perfect device to support your data science work is key. Members Online Citadel finances a new Texas stock exchange set to launch in 2025 Hello redditors, I have a Bachelor degree in commerce and I currently work in a non-finance company full time and day trade part time. In many ways the jobs are more similar than I thought. In an era dominated by data, the roles of data scientists and quantitative analysts (quants) have evolved into linchpins of decision-making across diverse In my experience Quant roles are definitely more varied, you have some shops that are more like dedicated applied data-science orgs. data scientist by looking at what they do, how they’re trained, what they work on, and how well they’re paid. For quant development, MS CS in tier-1 schools with great scores in competitive coding programs, participation/trophies from ACM ICPC type tournaments, etc. "Data science" has been a big buzzword the past few years and the field is only going to exponentiate throughout the decade. If a data scientist has an advanced degree in a related field, they may need to consider additional coursework or certifications in finance. 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. 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). P World - Using data science to uncover signals. ). Dec 6, 2023 · Education: A quant typically holds an advanced degree (Master’s or Ph. Though I can see Finance leading to very senior and executive positions in a company (e. Flexibility: With a solid math background, you can branch out into diverse roles beyond data science, such as quantitative analysis, cryptography, actuarial science, or academic research. Nothing goes to our traders. Please help me by comparing the two lines, I need a few data points. I basically work on feature engineering and ML techniques to solve business problems (fraud detection in financial markets). They don't care if you don't know a single bit of finance. ), but product analysts often have product intuition and domain knowledge that data scientists typically don't. At any given time, there are 10x more available data scientist jobs than quant finance jobs, and the data scientist jobs are far better. This transition would require additional learning and skills development, but the foundational knowledge and experience gained as a data analyst can be a great starting point. CFO), whereas Data Science would peak at something like a chief of insights/analytics for a company. 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). We’ll cover: Quant research roles are primarily for advanced degrees like Masters and PhD’s. in IB at risk management vs. Is that really all the difference between the two? Is a quant researcher just a data scientist working with financial and time series data? If not, what exactly does a quant researcher do? I am quite old (23), but would like to become a data scientist or a quant . But it’s mainly just cause data science jobs are literal dog shit and don’t actually translate to what “science” is with data. In my experience the signals generated by ML are often very different to those hand crafted via hypothesis testing. So if you're a quant researcher, coming up with models, you make a lot. If you experienced that massive market value increase, it was probably because the lack of experienced data scientists in the recent years. My background is in statistics and I currently work as a Data Scientist at a tax company. To be a quant trader wasn’t massively difficult, to become a quant researcher was. Citadel made 28B gross last year, and returned investors 16B net of fees. The perfect candidate is ridiculously difficult to find and usually the candidates we found demanded better work-life balance and fully remote roles. C/C++ is amazing and fast. Jun 9, 2021 · Winning top ranks in competitions sponsored by quant firms could also help you land interviews (e. Others are centered around delivering curated analytics and "selling" models to PMs or Traders. Work life balance. This is perfect for quant developer and quant trader roles. Again this is what got recruiters chasing and begging after the right candidates. In both my quant group and DS group, I collect data, build models using statistics and machine learning, and write production software. Not compiler design or low level programming, but enough to do a complete data science project from start to finish. I would say take numerical methods in python. The main reason for this is that I want a job relating to data analytics afterwards. Been learning more data science, ML and mathematical programming from free online courses to prepare for just job and interviews, not getting replies for interviews (applied to ~10 roles, being ML engineer/Data scientist/quant analyst) Now looking to enroll in a certification course which will help me in getting into a quant or ML role. Would it be crazy for me to switch? Especially if I am the type that likes structure, math, statistics, and only likes to do very light programming? People have mostly advised against it - saying I should try software engineering, data science, or sticking with trading, but my gut keeps returning to actuarial science. Quant work is like being a surgeon with numbers. If you're a quant dev, implementing those models, you make less. They will be responsible for setting the technical direction, leading projects, and mentoring team members. They typically work with the bank’s traders and are a middleman between the actual quant researchers and the traders. 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. 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. , market research, medical anthropology, public health, design research). Top quant funds hire only the BEST mathematicians, that is, olympians, top PhDs etc, most serious quant roles require PhDs. The quant dev roles are primarily C++/SWE roles so I didn't think that those align with my end goal of doing QR. I get you read black swan once but trust me, I work in data science and have been in the space since 2008. What (many) traders do: Traders will usually always have their eyes glued to the markets when they're on the clock. Sounds like the author might not have realized this upfront. 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). Quants definitely make more money, but not hundreds of thousands of dollars more, and it may be that data Dec 23, 2024 · The one advantage of quant stuff, as the work is very technical; the pay can scale very high. Python, pandas, numpy, sklearn etc. "Quant" is a basically meaningless term at this point, not unlike "AI". I feel like for quant research, you need much more math than typical data scientist to be successful though. Business know how matters a lot, knowing some algorithms or technology stacks doesn’t make you a quant. I am a bit of confused whether I should pursue Data Scientist or Quantitative Analyst as my future career plan. Not an existence argument. 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). Quant Researcher - the data science stuff While these are typical roles, the lines between the roles can get fuzzy at some places Going from 90% to 90. there are enough if you know who to listen to. I am thinking between NYU stern with a joint major in math and cs vs UChicago Econ and data science. It's a bit different now, as there are already a lot of data scientists with 1-2 years tenure, with increasing trend. but yes If I'm understanding correctly, it seems to be similar to the dynamic in the Data Science field. 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 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 Point 1) Quant finance is difficult (sophisticated mathematical models) and positions can be easily snatched up by math geniuses and/of PhDs: Usually there are 4 reasons why a certain field pays well 1) it is in demand at the time 2) it is hazardous (say climbing remote radio towers that are 100-200 meters tall), 3) it takes a lot of training (school + practical) and 4) High cost of living. 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. These people are typically the best paid quants as they work closely to Portfolio Managers and will So, either IC3s are vastly underrepresented in levels. A minor in Computer Science or Business Analytics would complement the major well. Current total comp is ~270k. In terms of preparing for a generic role as a quant. data scientist question is one that provokes significant online debate. 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. 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. Lower than F/G? Maybe, but I'd want to see the numbers to be truly convinced. However, I’m not sure as to which subject I should pursue a PhD in. They’ll call their “real” data scientists Research Scientists or Machine Learning Scientists. On the trading desk, we manage risk intraday and exercise some level of discretion in semi-systematic books, s Also keep in mind, most quant finance and data science classes start as a 4th year class or as a 1st year masters class. When I was working as a data scientist (with a BS), I believed somewhat strongly that Statistics was the proper field for training to become a data scientist--not computer science, not data science, not analytics. Also, there is a lot of cross-over with data science techniques such as Kalman filters, cluster analysis, and time series modelling. I've seen quant research jobs for a lot of finance companies. Have had data scientist in management consulting/Tech as one career path, and a quant researcher/trader at a hedge fund or bank as another career path. In this article, we compare quantitative analyst vs. Open to both academic and applied research. 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. 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. I’m in statistics not policy, but, my research thesis for my MS project is gonna be “nonparametric regression”, which would make any data scientist eye roll. I'm currently working as a software engineer in the data science team at a top investment bank. 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% If you have strong facility in quant and you have qualitative research skills, that could be very valuable in market research, user experience research, other types of consumer research, media and television research (either evaluating product or conducting background research to enrich the project), policy research, lots of other ones. Usually, they don't sound that different from a data scientist role, except focused on time series. 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. 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. As I said, I agree that research is really the most satisfying career, but it's not that easy to get into. If I choose to do Quantitative Finance, would that look weird with my engineering degree? I am considering Quantitative Finance in order to get into a Quant role afterwards. I’ve done a lot of preparation and research in anticipation of job interviews (thanks to this sub you guys are awesome) but I’m still not quite certain what the day to day differences are between a trader and researcher to see what role I might enjoy more. My 2c at least. Econ major is helpful if you want to go into macro trading or macro outlook. 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). 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. I was a trader but also worked very closely with the quant team. The big upside for Rutgers is that it’s in person and I’ll be able to do research, which I really wanna do. 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. The only thing is i was leaning to UChicago since it’s the holy grail for quants. Assuming you're aiming for something like quant research positions, the way I see it is that you would be hired for your research and problem solving skills, and not the specialist knowledge developed during the PhD. hedge and prop firms) and I can give you some insights i gained. From what I understand (admittedly never been on the sell-side, let alone a strat), it’s some mix of quant trader and dev, sometimes a quant researcher is in there as well. Quant Research certainly sounds up your alley, although you should start a studying regiment, afaik a big chunk of interview prep is having ironclad knowledge about all regressions, their assumptions, etc. it seems the average pay of quant is worse than SDE. Of course one shouldn't read it as "data science BAD" without any qualifiers, or that "data science-like quant" is bad. Hi all, I’m in a pickle. Because data science is a catch all term that means f*ck all these days. Deep learning, for example. The typical mid-career data scientist salary is $123,000 while the typical mid-career quantitative analyst makes about $139,000. Actually I would say it's more difficult to go into research in some fields than in quantitative finance. However, now that I'm doing a statistics MS, my perspective has completely flipped. Hiring a data scientist to join the company, especially under their conservative views of introducing data science into their work, can be pretty costly for the business, so in their perspective, having a temporary hire to help "prove" data science, is a more risk-free approach to adopting data science in their organization. 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. I have never worked in a quant fund and I don't know anybody who has, so I have no idea. 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. Your background is perfect, quant firms specifically looks for math/stats graduate, but PHD is usually preferred for a quant research role. 100K or whatever it costs is utterly immaterial vs say 200K starting comp not to mention 1mil later career. I have a degree in Math and am pretty good with python already so I can grasp the ideas pretty readily. It also helps to give a ballpark of their usual timeframe What are your responsibilities in those projects 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. Personally, I always found Quant work more interesting because there is a stronger appreciation for stats/math. The latter two don’t have many job availability’s besides quant and hence why quant roles are fillled with math and physics people. I've been taking this course online and the course is supposed to be a beginners introduction to Data Science and Neural Networks. Your degree will only get you the interview. This means reasonable costs. I believe a top recruiter would much prefer a sound knowledge of statistics than pure mathematics. As for tools that might be useful in quant and not data science, I can think of stochastic calculus, some advanced stats (e. . That said the most popular for this stuff is python from what I’ve seen in job listings and talking to other AMs. Algorithms/Data structures are also common interview topics for quant research roles so that's also something that would be good to know about. 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. It's a buzzword. 2) Quant Researcher intern at a leading hedgefund in Chicago - project not decided yet. Not the headquarters but still has a few hundred employees and a very big quant team. 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 OP is asking about ML researcher which is either a research scientist position or a ML Engineer position at Meta. 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. 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. UChicago has a fantastic mathematics and applied mathematics (they call it CAAM) program (source: went there) . This means reasonable turn around time. 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. A space for data science professionals to engage in discussions and debates on the subject of data science. Now, hee's the general rule about financial firms: the closer you are to the money, the more you make. One thing I heard about a quant career is that it usually requires a graduate degree/PHD and it's very tough to break in as a recent grad. But I heard it's not that simple to get into quantitative researcher (and other quant positions) for just Physics PhDs anymore. As a quant with around 14yoe, I tend to agree and disagree with the some of the comments here. My official title is a quant but a lot of my duties revolve around data science research. Yeah, a bit. It is possible to be competitive going the pure math PhD route but it's not an easy one. I don't personally believe that "data science ML methods will eventually replace Operations Research methods" because they are differing things. Statistics. Undergrad stats major/math minor. It really deppends how good you are. As others have mentioned quant researcher is a more statistically advanced role and does need masters + research experience or a PHD. 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… It really depends on what you want to do as a quant. 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". Whereas data-scientists are frequently just "sql-monkeys" with light coding skills in silicon-valley. 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. Does our work overlap? It's recommended to try both systems if possible and consider workflow, software preferences, and specific requirements. 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. quant traders are usually king in market makers, while quant researchers and quant devs are sidelined a bit. Classical "Data Scientist" has now become "Applied Scientist" or "Research Scientist" or even "ML Engineer" in some companies. 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). But you'll also always be locked in as the quant. D. 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. Rule of thumb is higher risk / higher reward based on how close you are to alpha generation and monetization. Feel pretty comfy with programming, but breaking into the field your in seems so ambiguous to me. I’m currently working as a Data Scientist at a large bank in Canada and know I have the technical, theoretical and business acumen to be a successful Data Scientist, however I’m eventually hoping to break into the US market and noticed that there seems to be a dreaded barrier to entry, a Masters degree. reddit's new API changes kill third party apps that offer accessibility features, mod tools, and other features not found in the first party app. CDOs are completely different disciplines. a quant role. I wanted to get into Data Science but my friend suggested since you have commerce background and day-trade already so get into quant finance and become full time trader. Jan 28, 2024 · Quantitative Researcher. 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. 100% agree although the math is a way to leverage another skill so definitely have something else that you are good at as well like programming / data science. 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 However, these individuals are not data scientists, they're operations research analysts with development ability (not on par with a software engineer). I am debating whether to take up that offer or not? I am not interested in this for any monetary gains. In the recent years data science was exploding, while now it's getting more saturated. Each firm draws the line a little bit differently between QR and QT, but traders are generally king. Based on the dataset, a career path in data science, particularly in a senior or managerial role within the finance or tech industry, and located in a major financial hub like London, would likely offer some of the highest salaries. Dec 16, 2023 · Photo by Annie Spratt on Unsplash. In Europe a machine-learning engineer tends to be more ml-ops. elementary probability and stats So it appears that in Silicon Valley MLE tends to be what European companies will refer to as a (full stack) data-scientist. ) and Data Science (Either in tech industry or finance or F500), which would be a lucrative option - In terms of - Compensation (Base+Bonus). g. Like if it was be a data scientist at a FAANG vs like being a quant researcher at a 3rd tier prop shop/market making firm I’d rather do the latter cause the work would be more interesting, regardless of if the former paid more. 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. 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. Depends on where you are (e. What most data science roles demand is the ability to communicate with the investment business, ie something akin to a L1. 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. However, I don’t want to be stuck in academia. 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) It's a frequency argument. learn about them and b. 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. Otherwise you had to be top scoring in maths and data science related subjects. fyi data, or alternatively (which aligns with what I've seen) you don't have a ton of people becoming true entry-level data scientists at facebook - instead coming in at an IC4 level. Among Investment banking, Sales and Trading, Quantitative Finance (Quantitative trading and quantitative research, quantitative development etc. data scientist wars when it comes to salary. It used to skew towards/cater to PhD hopefuls (will be starting a PhD next year) but the school has a ton of resources for recruiting and quant stuff (I started a quant club for example), although you have to be a bit proactive to a. I would be pretty surprised if that were true. Hey I am a data scientist looking to transition into this field. copulae), and optimization techniques. I am an incoming MS student deciding between programs. Quants tend to specialize in specific areas which may include derivative structuring or pricing, risk management, algorithmic trading and investment management. Jan 9, 2021 · The researcher will use some set of data to find signals, run backtests, and if a strategy is put into production, then a trader could be simply executing and monitoring it (if it's particularly systematic) or might be making active decisions to adjust certain strategy parameters and hedge the book against certain events / etc. Unpopular opinion here (don’t downvote me please): job market for computer scientists in US —especially those data science related areas— is so hot that most quant shops have been practically squeezed out of this market. , would help. We would like to show you a description here but the site won’t allow us. 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. As a quant, you do lots of pricing, risk, and a lot of model building. 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. I wanted to understand the difference between ML/AI in top banks Vs. Quantitative research is real data science to me Salary will be higher on the Data Science side for sure, especially starting out. Traders at banks can’t take discretionary risks so can’t really use our alpha models. Why quant then? I don't think that quant jobs give too many opportunities for that. A "data scientist" can be a simple data analyst using SQL to extract data, loosely use Python, make a power bi or tableau dashboard. Pros - Known to a pretty intensive program which i see as a fun challenge to take up and also try to get in par with the rest(who mostly come from a more math background than me - pure CS). Apr 23, 2025 · Quantitative analysis is the use of mathematical and statistical methods in finance and investment management. It is important to distinguish between financial skills and data science skills. All these domains are focused on optimising (business) decisions in similar but very distinct ways. You're probably better off doing investment banking, sales, trading, etc. Data mining and deep learning would be extremely useful in data science but not so much in quant. Dec 6, 2023 · Yes, a data analyst can definitely transition to a role as a Quantitative Analyst (Quant). Also data science is suuuuper hot right now that requires a lot of stats and probability. This is where time series/GLM comes into play Sounds like the second choice is up your alley. most top jobs are intense and stressful (doctor/lawyer/banker etc), tech being a big exception. The skillset isn't straightforward swap. 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. Welcome to the Data Analysis Careers subreddit, a para-community of r/dataanalysis for all of your career-entry discussion! We’ve received feedback and have noticed that the monthly career-entry megathreads did not get the attention that poster’s desired and the goal of this community is to help facilitate the needs of those just starting out on their careers. Quantitative traders may have different roles, but they're essentially traders that are implementing and executing quantitative strategies, though they are doing very little research and development. people work as quants for decades. Work environment and peers. 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. Only a few select firms like JSC recruit out of undergrad for Quant Research. Creating values with quantitative methods then you’re in I call them the data scientist and analyst, before the term was coined, it is essentially portfolio optimization and inefficiency finder. MS stats folks tend to go into data science, actuary etc, hence why they don’t fill up quant roles. The rest is coding and engineering skills (write clear code and not screw up the system. Others reference highly specialized infrastructure work. The work is somewhat research oriented. A community for sharing and discussing UX research. The goal is to think about UX research broadly and consider studies from related/overlapping disciplines (e. Recently I received offer for part-time QR Consultant at WorldQuant. 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. 1% distinguishes a decent data scientist from a great data scientist not really. And they’re both ranked well. I took courses like real analysis, complex analysis, linear modeling, financial time series, analytic geometry, and non-linear dynamics. 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). Also research and academia are unfortunately very traditional and bureocratic fields. 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. (from Nov 6, 2019 · eh, quant can be kind of the same way depending on where you end up. You are looking for data science position which are not the same. a good data science program could be better for breaking into quant than a lower ranked MFE program. A subreddit for the quantitative finance: discussions, resources and research. Data Scientist: Someone with extensive experience in data science, preferably in the banking or fintech industry. The manual research process is something like identifying some potential behaviour then precisely testing if it has value. Data scientist is too vague. b. Quant Finance is a very broad term though, and I imagine there is varying roles within the space (QR, Risk Quant, Pricing Quant, Data Scientist, etc) that you could pursue Now back to your question. The exposure of negotiations can lead to more interesting human work down the line. In Europe, it seems to me And you need to have decent programming chops. A person who does qual work probably respects it more than quant work, and vice-versa. 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. But that’s literally the stuff covered in book like elements or introduction to statistical learning lol. Yeah this is really crucial difference. My undergrad was in math and stats. Incredibly difficult I imagine. ) I am currently working as a full-time data scientist (for more than a year) and I want to pivot to a quantitative researcher(QR)/ Quantitative trader role. financial analyst is different from a BI analyst, etc. 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. I have opportunities to do ML in big tech or quant dev at some hedge funds. as for OP’s question it depends on the relative brand name of the two programs. NYU has the employment advantage of the city. By "quant" I mean quant research roles, which has very little overlap with "quant trading" or "quant dev" (sometimes these roles are mislabeled; a "quant trader" at Tower is actually a quant researcher). I’ll second everyone here and recommend Python. 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 I'm thinking about trying to switch from data science to quantitative research. I strongly recommend applying there. This means reasonable technical debt. Exit opps 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). I wanna do a PhD and then one of the careers that I’m aiming for is in quant finance research. They don't have tons of time In company 2, the data science would be shitty (unless it is run of the mill data science problem like spam/no spam, house price prediction, simple recommender engine etc). Prestigious, respected. I want to move away from being a SWE and do ML and ultimately hope to do quant research one day. for most people, quant IS the end goal, the dream job. Gathering insights from other data scientists who have made the switch can also be helpful. Working as a "quant" in HFT vs. ) in a quantitative field such as Mathematics, Physics, Engineering, Computer Science, Financial Engineering, or Quantitative Finance. 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. Dead useful for a lot of quant work. 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). Those working in the field are quantitative analysts (quants). My career path so far has essentially been data scientist -> actuarial analyst -> quant trader -> quant research. Did real analysis undergrad for mathematicians and it's way too theory focused for a dummy like me. I plan on getting an MS in statistics, so my idea of a career in either of these two paths would be for after graduating from an MS, not straight out of undergrad. Furthermore, you can get a data science job at a tech company, which is really competing with FAANG for work/pay. Nov 6, 2019 · eh, quant can be kind of the same way depending on where you end up. Want to work in AI/CS and math in finance. Members Online When the word is all about LLMs and GenAI and you are still using linear regression I am thinking of doing a masters in something related to data science and computer science. Physics geek here, who's worked in data science. As to general data processing and extracting signals, ML applications are already a thing. So keep that in mind. 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 You mention research in quantum computers. 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 also wasn’t deliberately making the transition. Eliminate factors such as institutional prestige, cost or alumni network, and simply look at statistics vs. When you go outside the field, chances are they respect the quant work more because it's more "science" than "art", but, when you go outside the field, chances are they don't realize how bad the "science" of a lot of quant research is. **New to research? top 5-10 MFE is the only masters really relevant for quant career - and widely represented in the industry. Quant Trader - people who develop trading tools, act on semi-automated trading (so using computers to generate signals, but humans double check the signals and hand trade them), adjust algorithm parameters, etc. It's also possible to start from trading or data science in finance and transition to quant roles in a few years once you've gained better quantitative skills and knowledge of financial Or a company will call the folks doing reporting and analysis “Data Scientists” to make themselves sound more advanced. "thrives in fast paced environment" in job ads is not a joke, if you don't enjoy intensity I'd recommend tech over quant finance. Whilst Data Science seems more statistics, python, SQL. Just a curious teenager trying to get a better understanding of future career options in stats besides from your usual data roles. I have had interviews for quant positions and they are mostly brain teasers, IQ tests, the required knowledge is C++, stochastic calculus, algorithms. 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. Quantitative Researchers are the quants researching for “alpha”. Quant Research/Data Science Salary at hedge fund I am 27M with MFE from top US program - think Baruch, Columbia etc. Even doing some self learning to make tiny trades or set up algos on my own leads to people with predatory intent for lack of better terminology. use them properly. Data Open, trading competitions, quant hackathons). Thanks to join HFT as Quant researcher you dont need C++ you just need really good financial modelling skills , this would mean you need to be able to analyze historical market data, create strategies to trade in the market and run PnL simulations of these strategies, select optimal strategies etc. To my knowledge, it seems that quant research is mostly glorified data science – from talking to QRs I’ve interviewed with, it seems like they mainly spend their time fitting linear models to time series, while traders tune parameter values so that their bots make trades that reflect their personal intuition and convictions, at least for I’m not in it for job titles vs just having a strong interest in mathematical finance. maktev ocip nho ktaz eiuysgb obtat uqvp bbgzdx nbuki dcmjw mxn bnjbnx umay ogfoyx oxuqyegr