Quant to data science reddit 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? 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. Most MFE grads/PhDs prefer Quant Research/Data Science based roles because of the lower risk involved as well as doing work more in line with their advanced degrees. Books like An Introduction to Statistical Learning and Hands-on ML (part 1) are great resources for this. 24 votes, 23 comments. so called alternative data) and even discretionary funds (ie. This therefore puts a much greater premium on software engineering skills. think that’s only true for phd level, coming in as an undergrad i think you have to know something about serious code design, most have a software internship the year before getting a quant research internship. 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). I came back and decided I would switch to data science, but I was worried I would miss out on the clear, predictable, generous pay of an actuary. Political science is the scientific study of politics. What companies want is Data Science to deliver value and this means putting models in production to drive real impact. Current total comp is ~270k. I just switched from quant dev to a "data scientist" and my job is more applied math (optimization problems, improving computational efficiency, stochastic modeling, with some statistics/ML). it seems the average pay of quant is worse than SDE. Furthermore, you can get a data science job at a tech company, which is really competing with FAANG for work/pay. Specialize in quant and learn the basics of the data science field. I’m starting my MSDA from WGU which will help with my python skills and I’m getting to the conclusion that it doesn’t hurt for me to apply ti the MS in Financial Engineering program. 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). I’ve generally found the people I work with that have MFEs bring in semi dated concepts. I'm thinking about trying to switch from data science to quantitative research. non-quants) are hiring data scientists to help them model the economy. Data science was still in its nascent stages and was more of a hybrid software engineering role at most places. but even without that should be no Quant Research/Data Science Salary at hedge fund I am 27M with MFE from top US program - think Baruch, Columbia etc. You're probably better off doing investment banking, sales, trading, etc. 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. It deals with systems of governance and power, and the analysis of political activities, political thought, political behavior, and associated constitutions and laws. For data science emphasize stats and ml knowledge over coding. 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. g. ) 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? 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. It’s been 6 months since starting a data science management role, and now have been laid off. 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). I agree that some questions raised doubts about actual applications but overall I felt tested rather than overwhelmed which is why I gave my opinion as such. What most data science roles demand is the ability to communicate with the investment business, ie something akin to a L1. 8M subscribers in the datascience community. Actuaries are a business role with some math knowledge. Generally speaking, both 'data scientist' and 'quant' have very different meanings across different companies and industries. Problem-Solving Skills : Math trains you to tackle complex problems and think abstractly, skills highly valued in top tech companies and research institutions. Someone has linked to this thread from another place on reddit: [r/algoprojects] Books on machine learning in quant finance If you follow any of the above links, please respect the rules of reddit and don't vote in the other threads. They are both not Data Science jobs but they're less competitive because people perceive them as less "sexy". 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. That being said, MFE grads have an opening for quant trading roles in the following ways: Data Science. 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. I was upset about the role but my boss assured me there were “big things” in the pipeline. I would be pretty surprised if that were true. Yes, an MS in Data Science. IMO a data science MS generally won't even be sufficient for the more technical data science/MLE jobs, unless you have a strong quantitative background prior to the program. For undergrad I think the most important electives for me was complex analysis (for learning about the intuition of higher-dimension modeling in machine learning) and non-linear dynamics (for understanding emergent complex behavior, which is very common in financial modeling). Data scientists are a technical role with some business knowledge. 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. , would help. (Info / ^Contact) 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. 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). The MCAT (Medical College Admission Test) is offered by the AAMC and is a required exam for admission to medical schools in the USA and Canada. physics phds from good schools who want to become quants can do it just fine. ) Depending on which sounds better for you I'd recommend trying to get a Data/Product Analyst (1. I was originally working as a space systems engineer designing satellite systems. CFO), whereas Data Science would peak at something like a chief of insights/analytics for a company. My salary is about the same as the quant developers (a little less but not much) I spend my time creating data pipelines for alternative data sources to improve forecasts, creating data warehouses for analysis, cloud security and architecture. While I do like ML, I hate anything to do with images, videos or text data. I'd expect a data science MS to be pretty surface-level on most of that material, since there's just so much material to cover in a short period of time. The #1 social media platform for MCAT advice. 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. Sounds like the author might not have realized this upfront. ) or Data Engineer (2. Members Online Data Science Manager to Backend Software Engineer I’m very grateful I saw this post because I’ve been wanting to get into the Quant Data Science world but didn’t know where to start. 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. 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. C/C++ is amazing and fast. You need the ability to apply quantitative principles to unknown sets of data. I want to enter in these following roles (Data Science, Data Engineering, Data Analyst, Quantitative Analyst). I am a Data Analyst for a reputable Wealth Management firm currently in my late 20s, with a background in Wealth, Asset Management & PE Consulting from a small unknown consulting firm but worked with several blue chip clients in the industry. 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. I have also realized that without any kind of domain knowledge, I am absolutely useless as a data scientist. You don’t need a finance back ground to work in quant trading. For quant development, MS CS in tier-1 schools with great scores in competitive coding programs, participation/trophies from ACM ICPC type tournaments, etc. I had to move into data science due to financial reasons. The level of business understanding required for a lot of data science work kinda makes junior data scientist a difficult role to create. true. Your degree will only get you the interview. Data sci may even be used as a tool for QF, so some skills can be transferrable. Only a few select firms like JSC recruit out of undergrad for Quant Research. I've got 2+ years of experience in Data Science/Software Engineering. S. A vague-ish answer is that data science is more broad whereas QF is more focused, like you mentioned: stochastic calc, volatility/ risk models etc. A subreddit to discuss political science. I found data science work to be far more interesting than actuarial work. Eliminate factors such as institutional prestige, cost or alumni network, and simply look at statistics vs. 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. It really depends on what you want to do as a quant. The finance and data world has broadened -- beyond traditional quant trading or quant research, buy-side firms are now using very broad ranges of data to trade (ie. 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. The areas of Quant Finance that I am most interested in are Quant Trading and Risk Analysis. 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 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. I've seen quant research jobs for a lot of finance companies. "Data science" has been a big buzzword the past few years and the field is only going to exponentiate throughout the decade. A space for data science professionals to engage in discussions and debates on the subject of data… IME, 70% of "real data science" is data cleaning / understanding what limitations and problems data have, which *to my knowledge*, is not typically reflected by kaggle competitions, but I could be wrong. I decided to do 2 Coursea courses IBM Data Science and Short Course in Machine Learning from UNSW if I want to put my foot through the door. #1 is my very first option and what I would like to do and #2 is more so of a backup. I got a master's in Statistics (integrated program with bachelor's), and things have worked out great. Of course one shouldn't read it as "data science BAD" without any qualifiers, or that "data science-like quant" is bad. Someone has linked to this thread from another place on reddit: [r/algoprojects] Quant/data science at physical commodity trader If you follow any of the above links, please respect the rules of reddit and don't vote in the other threads. In addition, most data analyst in general won't get to apply the techniques taught in the book. Quant research roles are primarily for advanced degrees like Masters and PhD’s. For example, at Meta, Data Scientists are essentially SQL/dashboard/analytics folks while at Google Data Scientists are typically stats and ML modelers. No entry-level Quant job will ever require Finance experience, some firms will literally reject if you do have it. 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. 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. If you are analyzing labor market data (or I’ll second everyone here and recommend Python. It was great! I am a bit of confused whether I should pursue Data Scientist or Quantitative Analyst as my future career plan. Knowledge-wise, it's beyond what an entry-level would need to know. Quant will be great, but volatile. ) position. Also keep in mind, most quant finance and data science classes start as a 4th year class or as a 1st year masters class. 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 This is my first data science test so based on my studies and my hobby applications of machine learning, I found I could be competitive when answering the questions. But for vice versa, not so sure. . 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. Thank you! If you want to go data science, brush up on your stats and ml knowledge for interviews. P World - Using data science to uncover signals. Working as a "quant" in HFT vs. 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. I call them the data scientist and analyst, before the term was coined, it is essentially portfolio optimization and inefficiency finder. During my masters, I got a data science internship at a (~1,000 person) tech company. I hope that this would be useful to some people. data science. Make sure you have some coding knowledge in R or python and SQL. ) I currently work as a data engineer for a quant team, my official title is quant data engineer. applied math for financial contexts. 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. 1. It is interesting work and pays well. Nov 6, 2019 · What are the advantages (stability, pay, employment opportunity, etc. The role was sold as a data science manager, yet ended up doing admin work and touched on very small amounts of actual data science projects. Working in quantitative finance, as a quant analyst, quant dev, quant researcher, or trader Working anywhere besides quant finance, as a data scientist. Data science will be more stable. With the rise of AI, code generation, text based prompts, IMHO Both fields will be obsolete in 10 years. This is where time series/GLM comes into play Sounds like the second choice is up your alley. 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% A community for people applying to, pursuing, or having completed a Master's degree in Computer Science or related programs (MHCI, MSDS, MSAI, MS ECE, MSBA, MCS, MIS, MEM, MSIM, MSOR etc. A minor in Computer Science or Business Analytics would complement the major well. /r/MCAT is a place for MCAT practice, questions, discussion, advice, social networking, news, study tips and more. This is probably quite a common question in this thread but I feel my situation is a little nuanced. P. The rest is coding and engineering skills (write clear code and not screw up the system. The work is somewhat research oriented. While my current role is far from it, I've worked with time series machine learning models on financial tick data during my university (Masters) days. 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. Lower than F/G? Maybe, but I'd want to see the numbers to be truly convinced. CDOs are completely different disciplines. Applied Data Science Lab by World Quant University: A great course to understand the concepts behind Data Science, learn advanced Python and showcase some real world projects. Data science also has the benefit of existing outside of the insurance industry where actuaries (generally) don’t. The one thing I worry about EU education is how much hands on education you get in a stats degree there. also looks like a lot of firms prop shops in particular would rather hire someone from a semi-target who does really well in the many rounds of technical interviews than someone from It is important to distinguish between financial skills and data science skills. I find the world of quant very fascinating because it gives the opportunity to work on dynamic and ever changing data. Academia was and continues to be getting more competitive at every stage of the process: increasing hiring/tenure standards without the compensation to match. That said, I'm sure it's useful for learning the stuff you mentioned in your post. 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. (Info / ^Contact) 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. Someone with a few years of experience in an analyst role who has cursory experience building ML models is probably going to be more successful in a “standard” data scientist role than a recent college grad who’s handy with ML but has very little 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. 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. Salary will be higher on the Data Science side for sure, especially starting out. So keep that in mind. in IB at risk management vs. Though I can see Finance leading to very senior and executive positions in a company (e. A space for data science professionals to engage in discussions and debates on the subject of data science. Why quant then? I don't think that quant jobs give too many opportunities for that. Usually, they don't sound that different from a data scientist role, except focused on time series. 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. Postings about current events are fine, as long as there is a political science angle. 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". luntzzyoqopqpsqllilxsbvjzdcceytbghzvcewghervcrrdqkjeivrubezmmavihpqfjhohibzb