Data science to quant reddit.

Data science to quant reddit 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 have a chance to study econometrics and data science (it’s not a double major) at a bachelors level and possibly continuing with graduate level degrees. Data Science. Personally for trading I prefer data science students over statistics. It might be the case for a data scientist to earn a DS matter. I am seeking entry level roles. But for vice versa, not so sure. Did real analysis undergrad for mathematicians and it's way too theory focused for a dummy like me. 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. I call them the data scientist and analyst, before the term was coined, it is essentially portfolio optimization and inefficiency finder. Your background is perfect, quant firms specifically looks for math/stats graduate, but PHD is usually preferred for a quant research role. Academia was and continues to be getting more competitive at every stage of the process: increasing hiring/tenure standards without the compensation to match. 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 seems the average pay of quant is worse than SDE. The third level is the people who can be called either data scientists or machine learning engineering who research and develop new algorithms. Yes, an MS in Data Science. You need the ability to apply quantitative principles to unknown sets of data. For example, at Meta, Data Scientists are essentially SQL/dashboard/analytics folks while at Google Data Scientists are typically stats and ML modelers. . The data science curriculum is being provided by the CS and Stats department and the electives you can take are from there too. Quant research is probably the toughest to get into because there are only a small number of positions and the pay is much better. No entry-level Quant job will ever require Finance experience, some firms will literally reject if you do have it. It is interesting work and pays well. 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 A place for redditors to discuss quantitative trading, statistical methods, econometrics, programming, implementation, automated strategies, and bounce ideas off each other for constructive criticism. 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% 1. That said, I'm sure it's useful for learning the stuff you mentioned in your post. 1. I believe a top recruiter would much prefer a sound knowledge of statistics than pure mathematics. 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. 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. 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. 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. I had to move into data science due to financial reasons. Physics geek here, who's worked in data science. You're probably better off doing investment banking, sales, trading, etc. 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 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. Current total comp is ~270k. Reinforced learning, autonomous car driving research, facebook's core data science group, etc belong to this category. While I do like ML, I hate anything to do with images, videos or text data. I've been trying to get into the quant industry (espc. 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. The areas of Quant Finance that I am most interested in are Quant Trading and Risk Analysis. 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. One university in my state, Stony Brook, has two masters programs Statistics and Quantitative Finance, both in the Applied Mathematics and Statistics School. P World - Using data science to uncover signals. Also data science is suuuuper hot right now that requires a lot of stats and probability. Data sci may even be used as a tool for QF, so some skills can be transferrable. CFO), whereas Data Science would peak at something like a chief of insights/analytics for a company. You don’t need a finance back ground to work in quant trading. Also everyone knows Harvard. 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). CSCareerQuestions protests in solidarity with the developers who made third party reddit apps. 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. Here in the equity alpha industry, there are several job types: data scientists, quantitative researchers, portfolio managers, and traders. Lower than F/G? Maybe, but I'd want to see the numbers to be truly convinced. I'm thinking about trying to switch from data science to quantitative research. Your degree will only get you the interview. This subreddit is for all those interested in working for the United States federal government. 8M subscribers in the datascience community. Nov 6, 2019 · What are the advantages (stability, pay, employment opportunity, etc. A space for data science professionals to engage in discussions and debates on the subject of data science. Mar 9, 2020 · By the time a data scientist graduates from a data science program, the data science landscape may have changed dramatically. Working in quantitative finance, as a quant analyst, quant dev, quant researcher, or trader Working anywhere besides quant finance, as a data scientist. 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. Depending on which sounds better for you I'd recommend trying to get a Data/Product Analyst (1. Why quant then? I don't think that quant jobs give too many opportunities for that. 24 votes, 23 comments. , would help. I would say take numerical methods in python. ” A MBA would be pretty useless for most quant roles, and may even hurt you in applications. My 2c at least. applied math for financial contexts. Political science is the scientific study of politics. From a contact I have who works with quants and is a trader she says that the general consensus with her work team is that always choose maths whether that is financial maths or statistics over finance if you want to break into quant roles. I got a master's in Statistics (integrated program with bachelor's), and things have worked out great. 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. 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). I've applied to Operations Analyst, BI/Business Analyst, and Quant Analyst roles on top of Data Scientist/Data Analyst. A space for data science professionals to engage in discussions and debates on the subject of data… 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. They don't care if you don't know a single bit of finance. The you can easily apply that in quant fi or data Sci. Ds in computer science or statistics, etc. 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 am a bit of confused whether I should pursue Data Scientist or Quantitative Analyst as my future career plan. 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. UChicago is very much a iykyk. The explicit barriers to entry are highest for actuaries because of the exams but I think quant research and data science attract better students. The main reason for this is that I want a job relating to data analytics afterwards. Knowledge-wise, it's beyond what an entry-level would need to know. I’ve generally found the people I work with that have MFEs bring in semi dated concepts. Furthermore, you can get a data science job at a tech company, which is really competing with FAANG for work/pay. I'm finding better results when searching "Junior Data Analyst" though. 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. I’ve seen thoughts such as “a CS major may pidgeonhole you into dev jobs, and a compfi minor may never be put to use. However, now with widespread hiring freezes and layoffs across big tech, is actuarial now a relatively better value proposition? It’s been 6 months since starting a data science management role, and now have been laid off. I hope that this would be useful to some people. Eliminate factors such as institutional prestige, cost or alumni network, and simply look at statistics vs. They often have Ph. 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 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. g. CDOs are completely different disciplines. 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. FAANG was hiring like crazy with huge comp packages and actuarial pay was fairly stagnant. 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. Top quant funds hire only the BEST mathematicians, that is, olympians, top PhDs etc, most serious quant roles require PhDs. Most quantitative analysts also have advanced degrees, but these tend to be doctorates in mathematics, economics, finance, or statistics. I originally thought a compfi minor would be helpful for breaking into the industry, but I’ve done some research, and I’ve seen that ML/statistics/data science/math experience might be more appropriate. During my masters, I got a data science internship at a (~1,000 person) tech company. Though I can see Finance leading to very senior and executive positions in a company (e. I was upset about the role but my boss assured me there were “big things” in the pipeline. Quant Research/Data Science Salary at hedge fund I am 27M with MFE from top US program - think Baruch, Columbia etc. 6 months ago, it looked like data science and SWE was the place to be. Of course one shouldn't read it as "data science BAD" without any qualifiers, or that "data science-like quant" is bad. 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 I was originally working as a space systems engineer designing satellite systems. Since the application process itself is often nothing short of herculean and time-consuming to boot, this place is meant to serve as a talking ground to answer questions, better improve applications, and increase one's chance of being 'Referred'. Working as a "quant" in HFT vs. 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 am thinking of doing a masters in something related to data science and computer science. I’ve got my eyes set out for either DS or Quant Research role in the future and I am taking BSci with DS and Financial Maths. The "mba brain" is real. Also keep in mind, most quant finance and data science classes start as a 4th year class or as a 1st year masters class. This is where time series/GLM comes into play Sounds like the second choice is up your alley. 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). I had a data science job as an undergrad at a tech company - it was completely different to my job now. 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). Data science was still in its nascent stages and was more of a hybrid software engineering role at most places. 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. I was laid off from a recent position and am considering an MFE as well as a career switch to data science/technology. Postings about current events are fine, as long as there is a political science angle. hedge and prop firms) and I can give you some insights i gained. ) 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? Oct 16, 2012 · I have worked in finance for internships and full-time (including quantitative research at a major asset manager and fixed income research at a bulge bracket bank). Actually, the job being so rudimentary was one of the main reasons I got a PhD; it was really clear that all the people doing the 'cool stuff' had PhDs and that they were on a different level to me. Sounds like the author might not have realized this upfront. It deals with systems of governance and power, and the analysis of political activities, political thought, political behavior, and associated constitutions and laws. #1 is my very first option and what I would like to do and #2 is more so of a backup. 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. 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. Currently majoring in Computer science and am looking at masters programs in my state that lead me towards careers in tech or trading. data science. ) Generally speaking, both 'data scientist' and 'quant' have very different meanings across different companies and industries. I have also realized that without any kind of domain knowledge, I am absolutely useless as a data scientist. 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. Below are some details about my background. 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. in IB at risk management vs. 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". The work is somewhat research oriented. true. In addition, most data analyst in general won't get to apply the techniques taught in the book. 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). Apparently interning/doing research every semester for 4 semesters (and summer internship) doesn't count as being a full year of work for some people. 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. It was great! Salary will be higher on the Data Science side for sure, especially starting out. 151 votes, 81 comments. Feel free to submit papers/links of things you find interesting. 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. If I choose this major, there is a concentration for financial econometrics/quant finance. I would be pretty surprised if that were true. ) position. UChicago’s applied data science masters is a whole separate thing from the actual CS and Stats department where rigorous classes can be taken. 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. Other common segments, such as pricing, structuring, and trading FICC products, broadly fall under the quant definition. A space for data science professionals to engage in discussions and debates on… Quant research roles are primarily for advanced degrees like Masters and PhD’s. It really deppends how good you are. Members Online Career advice: Data Science vs Software engineer for an accountant? Background: I am finishing a PhD in theoretical math who has wanted to work in data science for about 2 years, and began serious efforts to… The level of business understanding required for a lot of data science work kinda makes junior data scientist a difficult role to create. So keep that in mind. Only a few select firms like JSC recruit out of undergrad for Quant Research. Current program: MS Data Science at Vanderbilt It really depends on what you want to do as a quant. They are both not Data Science jobs but they're less competitive because people perceive them as less "sexy". For quant development, MS CS in tier-1 schools with great scores in competitive coding programs, participation/trophies from ACM ICPC type tournaments, etc. ) or Data Engineer (2. A masters in finance or financial engineering may help for general quant roles, but likely unnecessary for quant trading or other buy side roles. 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. A subreddit to discuss political science. fohr jbmx axwxupq uoc hvwnzel ypwyq xomxl fyvwbp mtipz znfxz rkpls enjwwb ovual rhkmp fabu