Can a data scientist become a quant reddit 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. The work is somewhat research oriented. Actually, teams care a LOT about winning. As a quant with around 14yoe, I tend to agree and disagree with the some of the comments here. Though I can see Finance leading to very senior and executive positions in a company (e. R is more data science specific, but capable of more advanced analysis. My bank data scientist offer is a lucky one to have especially given how brutal the market is (and me only being a fresh grad with no prior work experience lol). This is once again not possible without data science as you can dump all data together and start working on a higher level with less restrictions. But you'll also always be locked in as the quant. The only role you would need a more advanced degree is Quant Researcher (which is the true Quant). It’s very hard to find a Data Scientist role external to a company without prior Data Scientist experience. The #1 social media platform for MCAT advice. I. The field is asking for more education and PhDs are slowly becoming a necessary requirement versus just a preferred requirement. When people talk about getting a data science job without a grad degree, I think the general thought is that you can eventually become a data scientist, but you'll need to gain some experience first. It's one of the many reasons that "data scientist" isn't an entry level gig at most places -- you need to either have that theory in formal training (eg, econometrics at University), or have worked on real problems somewhere. I think there are more "tangible" benefits in the analytics position, but you really can't put a price tag on the exposure you get in IB. Even though stats and compsci are said to be better bets, *you* can get away with an MS in Data Science or Data Analytics because you already have respect and rigor from the math degree. Data science - covers a huge variety of topics, a lot of data scientists have areas where they tend to spend most of their time on. We would like to show you a description here but the site won’t allow us. The data scientist that I met taught the Bootcamp that I was in. It's very diverse and knowing what field you want to get into would also help you stand out. All of them have a masters. Economics is very good as bachelor’s degree, but it is not enough on the master’s level for data science. Undergrads aiming at this career typically have 1-2 internships and get their best top firm recruiting shot right out of college. A subject like applied mathematics, computer science or physics seems to be common among the PhD folks at our firm. I’m following the path that other quantitative analyst (who only have a masters degree) have taken. in IB at risk management vs. Mar 9, 2020 · What’s certain is that the quantitative analyst vs. There is so much more to launching a data product than just data science. Which I would rephrase here as "can I make quant dev/trader/research money with a data scientist background", using the "self-thought python programmer/hacker that runs SQLs queries in tableau and excel" definition of a data scientist and for the most part, the answer is "no". 2. I'm a data scientist trying to land a job as quant developer, do you think its easier to become a quant as datascientist or web developer? During this time you should be able to refine your soft skills and get a solid understanding of the non-data aspects of the business and how these connect to the technical skills you have, and how to drive value to the business through the application of technology and data science. , would help. Bioinformatics IS a data science, it's just that the data you work with is very niche and you may become highly specialized and used to working with these types of data. Data analytics is something you can move around with for the rest of your life. 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. In general, a QR will build models modelling the market, economics, individual assets, trading strategies and pricing derivatives etc. Now that you've learned that, here's how you can pay me. As a computer science major, this path is sort of more clear and feasible. CDOs are completely different disciplines. I had mathematics, statistics, machine learning, and a little computer science/programming, and I wanted a job where I could use all of that. 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. Working in quantitative finance, as a quant analyst, quant dev, quant researcher, or trader Working anywhere besides quant finance, as a data scientist. 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 Idk if i should major undergrad in data science or comp sci if i wanna become a data scientist. So far in 5 years at my job, I've used Python, C++, C#, VBA, and R. Also depends very much on the university. Eliminate factors such as institutional prestige, cost or alumni network, and simply look at statistics vs. This means reasonable turn around time. Data scientists can be in similar roles, but some data scientists are more business focused. Well after 3mo as manager of analytics, they changed my title to data scientist. Their strategies are probably more technically advanced and handle their pm/firm’s book size and risk tolerance better than yours Dec 22, 2023 · Start with an Online Data Science Course. Jan 18, 2024 · Plus, most data science hiring managers will not have time to research every data science certification they see on a résumé. 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. I’ve recently discovered quantitative finance and it looks way more interesting to me. Title creep/non-standardized titles. Yes, someone with a background in quantitative psychology can easily become a DS. It’s the best way to get a grip on everything you need to know about the data science field, learn a programming language and how to use structured query language, master data analytics, the tools you need to use, and the fundamentals of the field. Dec 6, 2023 · Yes, a data analyst can definitely transition to a role as a Quantitative Analyst (Quant). The problem with this sub is there’s too many CS undergrads creating an echo chamber amongst themselves making it seem like it’s a desirable Yes, starting a business does not guarantee success, but neither does pursing a quant career. Jan 28, 2024 · In this article, I will be sharing tips and the list of resources I’d use if I had to start over with becoming a Quant again. Data has to be managed, collected, cleaned, and organized in a way that enables data science. 70-80% of quant work is regression, stats, and optimisation. The exposure of negotiations can lead to more interesting human work down the line. g. Dive deep into finance industry, and try to become quant. Yes, you can pursue a data science career in finance. It's not unusual for top quants to have a PhD in math. Quant != data science. 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". 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 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). fyi, search for data scientists and look by company), but maybe you can hit that if you start off working for a FAANG (as opposed to working somewhere else first). Current total comp is ~270k. 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. social media sentiment traders outperformed old-school traders in the last few years. Some data science could help too. It really depends on what you want to do as a quant. Yes, you can become a Data Analyst with a Business Degree, especially with a concentration in Business Analytics. -As a data scientist you can work in anything. 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. Not so much for data science (although it may still have applications). I was originally working as a space systems engineer designing satellite systems. Complexity: while physics have very complex systems that are still not understood, data science offers cross-overs from different fields that yield interesting correlations. Classical "Data Scientist" has now become "Applied Scientist" or "Research Scientist" or even "ML Engineer" in some companies. The other type of Data Scientist role is R&D focused, pushing the boundaries from a technique perspective and turning this into tools/ libraries for other Data Scientists to use. For quant development, MS CS in tier-1 schools with great scores in competitive coding programs, participation/trophies from ACM ICPC type tournaments, etc. This has caused the supply of data science/statistician jobs to decline while the supply of interested applicants has never been higher (thanks to the current AI-craze). In company 1, the data engineering would be shitty. You will see a lot of Data Scientists with PhD’s or STEM based degrees for this reason. at the end of the day modern quant trading is more into data science rather than the old school of complex maths. You have data scientists who work in tech, political science, banking, public health, etc. Quant research is probably the toughest to get into because there are only a small number of positions and the pay is much better. 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 You need to pick up on management skills, and business in general throughout this time. I actually enjoy this. Get familiar with the "split, apply, combine" paradigm and have some practice setting up "pipelines" which are re-runnable (and therefore automate-able) sequences of data transformations that both prepared data for training and prepares data for predicting. Unless you’re a research or academia Data scientist then you’ll need that maths and stats. and it was the exact same job. 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. copulae), and optimization techniques. Now, someone may ask "but don't teams care about winning?". Prestigious, respected. ) I personally do a lot of coding, but despite my background, I'm not a "theoretical quant" -- I'm a working quant. 283K subscribers in the devops community. Data Open, trading competitions, quant hackathons). We’ll cover: So the question is, can you become a quant at 40 after successful career in science (physics)? I know that many will entino Jim Simmons (R. The master's in data science vs master's in CS is a stupid debate that people have on here because a lot of people feel threatened or feel territorial. What is making you consider this change? You should be qualified for data engineering roles outright, though experience optimizing queries and data pipelines and working with data analysts/scientists will be helpful to get more senior roles. Most résumés are only given about 30 seconds of review time. My advice: if you’re serious about pursuing a role in data science find a data-adjacent role where you can apply data science principles and build experience that way. I have had interviews for quant positions and they are mostly brain teasers, IQ tests, the required knowledge is C++, stochastic calculus, algorithms. 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. This is not a generic 'business' subreddit and off topic posts will be marked as spam. Quant Research rarely hires undergrads. CFO), whereas Data Science would peak at something like a chief of insights/analytics for a company. Her data science program had a lot more emphasis on statistics as well as databases and data management (more SQL, JSON). First, let’s talk about the general skillset for becoming a 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) Jan 10, 2025 · Becoming a quant researcher is hypothetically possible, but you should be targeting a PhD for that instead. I am an incoming MS student deciding between programs. #1 is my very first option and what I would like to do and #2 is more so of a backup. Generally speaking, both 'data scientist' and 'quant' have very different meanings across different companies and industries. b) I think it depends on which area you want to work on. Which is actually a good thing because that gives you the opportunity to grow. I call them the data scientist and analyst, before the term was coined, it is essentially portfolio optimization and inefficiency finder. just look into the quant positions job feeds, most of the requirements are similar to a data scientist specialized in finance. MS stats folks tend to go into data science, actuary etc, hence why they don’t fill up quant roles. 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. With the rise of AI, code generation, text based prompts, IMHO Both fields will be obsolete in 10 years. Challenge for getting hired, is setting up a track record showcasing performance. a) There are several good services nowdays which gives you infrastructure with data/historical data and API for order execution. You need to pool skills from various companies and iterative improvements to transition into a direct, full-time senior data scientist, or a data scientist manager. Government funding/contracting for all kinds of things is starting to include large data science components. For example, may you start off with crime data, then move to marketing, then to investing, then to survey work, etc. • PhD in Math, Computer Science, Engineering, Data Science, Physics, or other related field • Proficiency in C++ and/or Python • Experience with the latest machine learning techniques My first thought is that Law is a profession and Data Analytics is a tool. I strongly recommend a book called “The Fourth Paradigm” if you want some of the history of the discovery of data science, as well as various essays that cover some prime examples of its application. Working as a "quant" in HFT vs. -Most data scientists either use R or Python. 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. As for tools that might be useful in quant and not data science, I can think of stochastic calculus, some advanced stats (e. Going from 90% to 90. 1. If research scientists sounds like hard to get role, its not. I have seen a lot of people who became data analysts with business degree, since for most positions its enough to know stats until regressions, R Data analyst - usually people making reports and visualizations and usually less technically inclined. Qualifications •Master’s degree in Computer Science, Data Science, Engineering, Information Systems, Analytics, Mathematics, Statistics, or a related field •Must have 36 months of experience in the job offered or in a related occupation I'm thinking about trying to switch from data science to quantitative research. The better the school you're in the more CS you're doing and the more suitable this is as training for quant work. This is where time series/GLM comes into play Sounds like the second choice is up your alley. I went the opposite direction (data science/data engineering -> software engineering (ML)). Initially, from looking at recent hires at large firms and asking around, it seemed that a PhD in stats would be the “safest”/most popular route, followed by something called operations research (which is essentially just applied math). The projects in my portfolio involves a lot of data scraping, data cleaning, automated financial analysis, basic ML that tries to predict things and recognize patterns and images, chatbots using Llama There’s are software engineers and quant developers who implement the system. There probably big difference between actively trading hedge funds, and slow investment funds which rebalance portfolio once in a while. I see the sort of work that just dies in a notebook and a ppt as becoming more of a data analyst specialty. You would be shocked at how many guys we get with incredible CVs that can not write a function to parse a date string. To answer you question, is there jobs inherently similar? It wasn’t particularly difficult for me, depending on your definition of quant. This means reasonable technical debt. Some new tenders are nearly all data science related. Many employers value practical skills and experience, sometimes more than the specific This is probably quite a common question in this thread but I feel my situation is a little nuanced. P. Do you mean quantitative development, or quantitative trading? The former is certainly doable with a solid CS resume, a lot of hires that go into quant dev at Two Sigma/Citadel/Jane Street also recruit and get offers for Big N companies like Google and Facebook. but being a DE isn't so domain specific. Note that the vocabulary used in psychology differs a bit from stats or CS, so just review materials from those fields to make sure you're speaking the same language during your interviews. Q: I know what type of quant I want to be, can you be more I about what I should study? A: There was a detailed discussion of this in this post. Once you get your first job as a data scientist, I can honestly say that you realize how much you don't know. Thoughts? Edit: Formatting. If you want to become a really top level quant, like the ones who get paid a shitton of money, some amount of graduate school is probably required. 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. Yes, you can. 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. You are only 25 so if you do want to become a quant, I don’t see why not. Some skills you use as a DE may be useful in building automated pipelines that feed a quant's models. 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. If you are really new to the field of Data Science, take a short online prep course. I stayed as data scientist for 3mo and then found another data scientist job where I could get better pay. Whilst Data Science seems more statistics, python, SQL. I also think i'm better off just doing masters in finance/economics and switch area to something that provides more long-term skills than get stuck as data-business analyst for Sure. Data analyst and data scientist are slightly different skillsets, and one doesn't necessarily flow into the other. Dead useful for a lot of quant work. I really enjoy my work and what I build/study. 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. Assuming you have the requisite technical skills (coding, data analysis, math), I think the practical aspects of engineering programs are well suited for working as a quant. The areas of Quant Finance that I am most interested in are Quant Trading and Risk Analysis. ), but product analysts often have product intuition and domain knowledge that data scientists typically don't. I'm okay to stay at NYC or jump to west coast. Data Science is difficult and often requires hard work, talent, and real investment to get right. My ultimate goal is to eventually work on risk modeling or to become a quant (highly unlikely, I know), althout I'm also interested in private equity. What I am wondering is whether a company is willing to take the risk and hire you a this age. Dec 23, 2024 · The one advantage of quant stuff, as the work is very technical; the pay can scale very high. Had to do a masters then PhD to get into quant research though. He was extremely knowledgeable in many aspects and had great communication skills. The explicit barriers to entry are highest for actuaries because of the exams but I think quant research and data science attract better students. This is a place to discuss and post about data analysis. Kinda boring imo, but can be a good entry level job. I got my undergraduate in math and a masters in business and data analytics (switched from the actuary track). It’s said that ~80% of ML models these days never make it into production. The program trains you in Python, SQL, and R. Take calc 1, 2, and multivariable and see if you can get A’s for all of them. It's a frequency argument. My career path so far has essentially been data scientist -> actuarial analyst -> quant trader -> quant research. I would say data science is more skewed to R though. DS emerged from the problem of relying on massive amounts of data that can become corrupted, improperly collected, etc. Once you are a research scientist, you can then get heavy ML/Dl applied Data scientists role. Honestly, it doesn't really matter the major name. 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. I am a senior data scientist with about 6 years of experience. In this article, we compare quantitative analyst vs. What distinguishes a great data scientist from a decent one is the ability to solve the right problem in a sensible way. Start with QR and become a PM at a HF. There is a lot of overlap between quant trader and quant researcher though, and where exactly the roles differ changes a bit from firm to firm. This is my career. If you want “a lot of options” and your undergrad “business school” is that good (hint, it probably isn’t esp in the eyes of top firms, people don’t respect Probably want to take some math courses, specifically probability and statistics. If you can’t ace the coding test you aren’t getting in, regardless of any qualifications. I have also realized that without any kind of domain knowledge, I am absolutely useless as a data scientist. You can always discern it's "How to become a data scientist in X months" when all they advise you of is circular. While I do like ML, I hate anything to do with images, videos or text data. I am a recent masters graduate in Masters in Data science and this is the job description. People don't want to hear this, but you just have to be smart, and have good intuition for probability, game theory, and maybe some mental math. I did my math undergraduate at an engineering school and was just a few classes shy of having a second degree in mechanical engineering. I’ll bet 90% of “Data Scientists” aren’t developing their own models, but rather tuning hyper-parameters. Dec 6, 2023 · Can a Data Scientist become a Quant? Yes, it is possible for a data scientist to transition into a quantitative analyst role, often referred to as a "quant". One companies senior data scientist may be another’s data analyst. Business Intelligence is the process of utilizing organizational data, technology, analytics, and the knowledge of subject matter experts to create data-driven decisions via dashboards, reports, alerts, and ad-hoc analysis. I’m giving this advice because I don’t view this as my job. AutoML really has changed the world and industry. The thing is, in nowadays job market, "Data Analysts" titles are paid less. data scientist question is one that provokes significant online debate. 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 There were no employees under me. However, I do not and have worked my way up through an internal transfer. You can be a quant, or you can be a statistician, or a data analyst, or specialize in ML architecture, software engineering or development, etc. This means reasonable costs. I had to move into data science due to financial reasons. Data science is growing outside of tech. 1% distinguishes a decent data scientist from a great data scientist not really. What matters is your course content and curriculum. As much as a data engineer can become change professions and become anything they want given time and energy investment to learn the new trade. 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 Hi all, I’m in a pickle. I get you read black swan once but trust me, I work in data science and have been in the space since 2008. Q: Does Data Science count? A: Data Science is typically statistics + business + CS. deep learning, recommenders, web analytics, etc. Data science just wasn’t cutting it, so I interviewed and got an offer. I can use whatever language I want. That being said, MFE grads have an opening for quant trading roles in the following ways: Starting out at firms where quant trading is effectively quant trading+quant research, and transitioning to a pure quant trading kind of role at a different firm. You don't need to take specific classes or a major to become a Quant Trader, you don't even need to know anything about finance. Ds in computer science or statistics, etc. If you aren't at a top school or if you aren't exceptional (or very lucky) getting a quant job right out the gate will be hard, so you first need to show that you are actually good on paper. Now when i look at data analyst positions with same descriptions, i just can't really see how can someone transition into data science doing that type of useless stuff. Like I said, it gives you the tools you need to pursue any STEM field that you desire and it's up to you to really choose the area you want to focus on, and, as you could probably guess, I chose quant People with Psych degrees can become a data scientist. The latter two don’t have many job availability’s besides quant and hence why quant roles are fillled with math and physics people. Learn both. I expect my data people to know basic maths and stats. The only commonality is that you’re developing models to understand data, there’s a lot more that goes into being a good quant You will of course need to supplement what you learn on the job outside of work to become a data scientist, but don't be the muppet applying to jobs you don't have a cat's chance in hell of getting - be realistic and realise getting the role you want isn't necessarily a linear process Similarly an experienced programmer can't equal a data scientist with deep math/stat knowledge. Quant work is like being a surgeon with numbers. I wanted to work on interesting problems and to use a wide variety of my skill set. Then apply to internships. Both roles require a strong foundation in Mathematics, statistics, and programming. Getting a job in data science eventually vs. I can’t speak for other funds but our interview process is very very coding heavy far more than it is quant and finance heavy. Furthermore, you can get a data science job at a tech company, which is really competing with FAANG for work/pay. The third level is the people who can be called either data scientists or machine learning engineering who research and develop new algorithms. I saw it all the time in the wild when I was consulting. For example, at Meta, Data Scientists are essentially SQL/dashboard/analytics folks while at Google Data Scientists are typically stats and ML modelers. So, you want to get the "Data Scientists" title. How to go from credit risk data scientist to quant analyst/trader So I have about 5 years of experience in credit risk data science, I mainly develop scoring models and time series models. 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). Reinforced learning, autonomous car driving research, facebook's core data science group, etc belong to this category. Are you equipped to develop stable diffusion/deepfake tools? Probably not (although you could learn). Jun 9, 2021 · Winning top ranks in competitions sponsored by quant firms could also help you land interviews (e. QTs take this 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. I'm finishing up Oregon State University's MS in Data Analytics, which is basically a computational stats degree with a computer science core. Salary will be higher on the Data Science side for sure, especially starting out. So keep that in mind. Okay, the pro is my life horizon will be greatly expanded, where I could network with different types of either tech or non-tech elite or excellent ppl. Your degree will only get you the interview. 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. That is, be good at CS and you can score these through a standard interview process. I'd you want to self trade lieka quant, you actually can self learn and then practice. financial analyst is different from a BI analyst, etc. data scientist by looking at what they do, how they’re trained, what they work on, and how well they’re paid. Ack: The average IQ for a Data Scientist is 113, which is the highest average. Yes, I did UG actuarial science, did 1 year grad as a data scientist then an actuary at a pension fund. All in all, the transition from Big Tech to Quant happens relatively often, and your background is fine for most roles as long as you do well in interviews (which are significantly harder than Big Tech), you can land a very good job. Specialize in quant and learn the basics of the data science field. . 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. They often have Ph. But there have been one or two ideas which I thought could very well have be a top 5 if I could talk about it in public. But as a quant, I can't think of a single application (which doesn't mean none exist -- I just can't think of one. Maintain a high gpa, do a lot of leetcode and greenbook, get an internship at FAANG, then get an internship at a quant firm. ), but he built his own company. I support a lot of data scientists but I wouldn’t call myself a data scientist any more. How does someone become a quant after obtaining a data science masters degree? What additional steps are required? I’m expecting to graduate with a data science masters around December 2023. I am seeking entry level roles. immediately becoming a data scientist are different things. How to become a data scientist > learn the skills of a data scientist. How to Transition from Data Analyst to Quant The level of business understanding required for a lot of data science work kinda makes junior data scientist a difficult role to create. It's a bit different now, as there are already a lot of data scientists with 1-2 years tenure, with increasing trend. 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 P World - Using data science to uncover signals. 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. Learn ML/DL, and then get a job titled "research scientist", that's way more specific than a "data scientist" title. Also if you can PhD in any science related field with some math involvement, then getting hired becomes easy. To be a quant trader wasn’t massively difficult, to become a quant researcher was. This is a data science subreddit, not a business analytics “flex on my boss” subreddit. The math isn't usually very deep, but you have a lot of data and compute is basically free, and a lot of the complexity is around how to use that to push the data further. The math/physics graduate students do research to uncover strategies to make make money - this is the relevance to trading. A senior data analyst will know way more about how to interpret data, model outputs, and business use cases than a junior data scientist, even if they don't know the finer details of Spark or whatever. data Most quantitative analyst have a PhD but a good percentage worked their way into the role. You won't be the negotiator. Below are some details about my background. Very solid. but even without that should be no 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). There’s very important differences in the skill sets. Data science is increasingly being used in the finance industry for tasks such as risk management, fraud detection, algorithmic trading, and customer analytics. Apart from the classical spreadsheets and office and BI tools, my tech stack includes python (with data science and data viz library) and SQL. Its just a job at the end of the day, we just want cool, and smart coworkers who we can get a beer with and hear a new idea from. 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). I am quite old (23), but would like to become a data scientist or a quant . Then did a year in quant trading. ). Python is generally considered easier to learn but had wider capabilities. At any given time, there are 10x more available data scientist jobs than quant finance jobs, and the data scientist jobs are far better. The work I was doing was so ML heavy that I asked that my title change to data scientist. What I meant to say that within the context of "data science", a path the OP is hoping to take, that data analytics generally forms part of a broader data workflow and that is rarely done in Excel because it needs to merge smoothly with many data engineering/science tools and frameworks that Excel isn't ideally suited for. Both fields are pretty competitive. Hey everyone, I’m (33) currently a quantitative analyst on track to become a data scientist. "what jobs in quant finance can data scientists land?" For mature grads doing a quantitative masters, or moving from data science related tech roles are all legitimate and common paths. You are confusing Quant Trader with a Quant Researcher. Your experience with programming languages like SQL, Python, and R is valuable and aligns well with the skills required for data analyst roles. Not the easiest, but it never is. Even though it sounds you skipped the internships and this year's recruiting season, still might be best to give it a try and try to get a job, if only for interview experience, as long as you have some basic familiarity with standard quant interview material Besides "Research Data Scientists" (developing novel approaches, writing scientific papers, conducting researches for R&D), all "Applied Data Scientists" are in a sort of sense "Data Analysts". Rules: - Career-focused questions belong in r/DataAnalysisCareers - Comments should remain civil and courteous. I think Data Analytics would give you the most flexibility. In the recent years data science was exploding, while now it's getting more saturated. Can you deploy ML models and do statistical methods to analyze or predict economic trends? Probably. It’s not AI, but more focused on making sense of new massive data availability. Current program: MS Data Science at Vanderbilt Experience wise, a MS + 6 years seems to be on the low end of what would qualify you for the levels that would get you to that comp (you can go to levels. If you go into industry straight out of undergrad, it's hard to get multiple years where you can basically just solve one big problem entirely on your own, mostly with limited feedback. So even if your university-based certificate is actually worth something, recruiters likely won’t notice it. But I've hard that data science at a bank can be boring as shit which worries me as I do want to be challenged intellectually even if the above is a bit too much for me. Not an existence argument. I would rather go for statistics, econometrics or actuarian science, or data analytics / data science degrees, or vocational degrees such as financial data science, marketing data science etc. Personally for trading I prefer data science students over statistics. Unless you want to be a quant dev, computer science isn’t really the best field to do if you want to be a quant. And now I’m in a somewhat unique data strategy and architecture role. What is much less clear is whether a data scientist can squeeze enough value out data and modeling to actually impact that, especially when you are comparing the value to that of hirng like another analst, trainer, therapist, etc. 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. /r/MCAT is a place for MCAT practice, questions, discussion, advice, social networking, news, study tips and more. Depends on where you are (e. Data Science = collection of tools you learned to solve a problem. I hate gater-keepers. To learn data science for a finance career, I recommend enrolling in courses at TutorT Academy. I also wasn’t deliberately making the transition. If you actually want to be quant go to a lower ranking school, major in math/physics, and then work your way into trading. The only commonality is that you’re developing models to understand data, there’s a lot more that goes into being a good quant You will of course need to supplement what you learn on the job outside of work to become a data scientist, but don't be the muppet applying to jobs you don't have a cat's chance in hell of getting - be realistic and realise getting the role you want isn't necessarily a linear process Quant != data science. Maths, physics, stats, engineering, data science etc are all far more beneficial. physics phds from good schools who want to become quants can do it just fine. Data science will be more stable. Regardless though, my end goal is to work as a quant (or possibly a data scientist). I’d imagine it’d be the same for Internships as well. Tbh anyone trying to become a quant should have a backup plan: especially someone starting late. 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. 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. It's very hard to do meaningful work if you have no grounding in a domain-specific theory (eg, some social or physical science). 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 I'm understanding correctly, it seems to be similar to the dynamic in the Data Science field. Quant will be great, but volatile. If you experienced that massive market value increase, it was probably because the lack of experienced data scientists in the recent years. Where I am studying, quantitative specialization of business degree - Business Intelligence, Business Analytics, econometrics or Data science are all viable options even on job listings. The difference between a failed data science project (never reaches prod and makes a difference) and a successful one is usually the amount of clever tricks the data scientist has mastered and could apply. foiwrp xuhrzxes pajsg wrrz yudvaq ilbxgk bpvo jcjayei xohhbz ojuhww zlmkvcp obiacv wcdwt plgut rhcoj