Quant vs data scientist reddit.

Quant vs data scientist reddit My 2c at least. 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 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. In finance, career options are more limited. 2) Quant Researcher intern at a leading hedgefund in Chicago - project not decided yet. Some people claim that they are data scientists when they are actually excel jockeys. data Sounds like the author might not have realized this upfront. Otherwise you had to be top scoring in maths and data science related subjects. Hi I'm now working at a fintech in NYC as software engineer. Will this also be open to a data scientist on a L/S team? The level of business understanding required for a lot of data science work kinda makes junior data scientist a difficult role to create. More math is always better you’ll find, especially if you consider graduate and professional level econometrics work. Depends on where you are (e. I've seen quant research jobs for a lot of finance companies. It also helps to give a ballpark of their usual timeframe What are your responsibilities in those projects 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. Quants tend to specialize in specific areas which may include derivative structuring or pricing, risk management, algorithmic trading and investment management. 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. 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. A masters in finance or financial engineering may help for general quant roles, but likely unnecessary for quant trading or other buy side roles. My grad school is giving me the option to count several of my finished data science courses toward accelerated completion of a data science masters — estimated finish for that would be December 2023. applied math for financial contexts. 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). Data Scientist: Someone with extensive experience in data science, preferably in the banking or fintech industry. You can earn great money though, and real data scientists are much more difficult to find compared to accountants. I call them the data scientist and analyst, before the term was coined, it is essentially portfolio optimization and inefficiency finder. All these domains are focused on optimising (business) decisions in similar but very distinct ways. 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). Are you actually writing code? Familiar with pytorch/tensorflow? Know when to employ a random forest vs KNN? Physics/data science are pretty sexy degrees for quant roles. They will be responsible for setting the technical direction, leading projects, and mentoring team members. From my experience quant devs who stay in finance only progress into other quant roles, but they might move into a senior SWE role or something if they leave quant. I don't personally believe that "data science ML methods will eventually replace Operations Research methods" because they are differing things. A MBA would be pretty useless for most quant roles, and may even hurt you in applications. g. Again this is what got recruiters chasing and begging after the right candidates. Source: Am a Econ graduate doing data science and econometrics in the industry. Data science is a new area, still kind of messy, with lots of charlatans and brilliant people all competing for the same jobs. 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 reminiscent of many quant roles selling themselves as something fancy mathy while in the end being very similar to a data science role. 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? Generally speaking, both 'data scientist' and 'quant' have very different meanings across different companies and industries. 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. Salary will be higher on the Data Science side for sure, especially starting out. 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 I have met business analysts who act as really good data analysts and coders, and I've met data analysts whose role may involve serious quantitative understanding but little implementation of deep analytical methods and little digging upstream into source data. 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. Data sci may even be used as a tool for QF, so some skills can be transferrable. However, now with widespread hiring freezes and layoffs across big tech, is actuarial now a relatively better value proposition? The third level is the people who can be called either data scientists or machine learning engineering who research and develop new algorithms. . My 2p. #1 is my very first option and what I would like to do and #2 is more so of a backup. b. Some companies and industries use the title for Data Analysts who have a minimum proficiency in scripting languages. 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). Physics geek here, who's worked in data science. Reinforced learning, autonomous car driving research, facebook's core data science group, etc belong to this category. From reading a few thousand job postings over the last few months, it appears that most positions that were specifically recruited from econ MAs or PhDs ten years back are targeting Computer Science BA/BS + Data Science/Data Analysis MAs these days. Did real analysis undergrad for mathematicians and it's way too theory focused for a dummy like me. The "mba brain" is real. What is your work mode (e. A long time ago I worked in an accounting role, and I thought it was easy. 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. It is important to distinguish between financial skills and data science skills. ) Hi all, I’m in a pickle. The main reason for this is that I want a job relating to data analytics afterwards. A minor in Computer Science or Business Analytics would complement the major well. 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. 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. Working in quantitative finance, as a quant analyst, quant dev, quant researcher, or trader Working anywhere besides quant finance, as a data scientist. Your degree will only get you the interview. This also includes ML models like PCA as well as other models like HMM. "Data Science" has a pretty ambiguous meaning these days. I would say take numerical methods in python. Data scientists are a technical role with some business knowledge. They often have Ph. 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 generally found the people I work with that have MFEs bring in semi dated concepts. What separates the two is more in regards to the questions they are answering. I am an incoming MS student deciding between programs. The rest is coding and engineering skills (write clear code and not screw up the system. However since I came from an analytics background, I'm always interested in mathematics and machine learning. I am a bit of confused whether I should pursue Data Scientist or Quantitative Analyst as my future career plan. ) 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? 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. But for vice versa, not so sure. I came from another STEM background and did a MSc in Computer Science DS major in the UK two years ago. 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). In a nutshell, Data scientist mainly analyse data, build ML models and convey analysis to stakeholders whilst Data Engineers build and maintain data pipelines, and organise the data so it can be used by Data Scientist. The you can easily apply that in quant fi or data Sci. Preference: Math, Statistics, Operational research, computer science, (edge profile) Engineering Capital Quant A capital quant works on modelling the bank’s credit exposures and capital requirements. I don’t really plan to work as a librarian because either data scientist roles or quant finance roles would pay twice as much. Go for the Data Science or Data Analysis program instead. Feb 13, 2019 · 2/ whats the difference in work between a quant vs data scientist at large quant firms like twosigma/deshaw/citadel etc 3/ traditionally, quants and fundamental analysts are considered front office and have the opportunity to transition to a portfolio manager role and manage money. 6 months ago, it looked like data science and SWE was the place to be. 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). 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). The tricky part is that you'll find Data Scientists (by title) who don't do any predictive (or prescriptive) analytics. You don’t need a finance back ground to work in quant trading. Though I can see Finance leading to very senior and executive positions in a company (e. Actuaries are a business role with some math knowledge. Personally for trading I prefer data science students over statistics. Career path: Quant vs Data scientist. I am thinking of doing a masters in something related to data science and computer science. ), but product analysts often have product intuition and domain knowledge that data scientists typically don't. FAANG was hiring like crazy with huge comp packages and actuarial pay was fairly stagnant. The skills of statistics and I found data science work to be far more interesting than actuarial work. Also, there is a lot of cross-over with data science techniques such as Kalman filters, cluster analysis, and time series modelling. Of course one shouldn't read it as "data science BAD" without any qualifiers, or that "data science-like quant" is bad. Most UK MSc Data Science probably won’t cover a lot since they are mostly 1-year course. Quant research is probably the toughest to get into because there are only a small number of positions and the pay is much better. Apr 23, 2025 · Those working in the field are quantitative analysts (quants). Oct 16, 2012 · Can anyone comment on the availability and stability of jobs in finance vs data scientists? Several studies have projected a shortage of people with the skills of analyze massive amounts of data in the upcoming years. Data science also has the benefit of existing outside of the insurance industry where actuaries (generally) don’t. "Data science" has been a big buzzword the past few years and the field is only going to exponentiate throughout the decade. Data science was still in its nascent stages and was more of a hybrid software engineering role at most places. Problem-Solving Skills : Math trains you to tackle complex problems and think abstractly, skills highly valued in top tech companies and research institutions. As far as 'data scientist's vs 'data analyst' - I probably fall into a different camp than most. Urgently looking for recent PhD graduates to join a Quant Research group at a top HFT firm in New York $150,000-$350,000 in total compensation Key Qualifications: • PhD in Math, Computer Science, Engineering, Data Science, Physics, or other related field • Proficiency in C++ and/or Python Posted by u/datadataguy - 24 votes and 14 comments A fundamental understanding of computer science or data science would help too. Creating values with quantitative methods then you’re in 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. What most data science roles demand is the ability to communicate with the investment business, ie something akin to a L1. i have a BS in Data Science and employers also didn't care, but since it's an engineering/CS degree I landed a data engineer job out the gate and then got lucky in a department where my stats skills could shine, so now I'm leading the ML algorithm decisions for the team 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. An analysts answers questions about the data, whereas a data scientist answers questions about the business from the context of data. You need the ability to apply quantitative principles to unknown sets of data. Data Science. Happy to change it if you feel differently, but that’s not the case from my experience and I’m not too sure where else they might move up too bar maybe another quant role. Now landed a UK Data Scientist role with an above average salary given my zero related experience. Data analytics is a really broad field, and you can specialize in lots of different subfields and tools. I want to pursue something in the field of data science. 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. 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. CFO), whereas Data Science would peak at something like a chief of insights/analytics for a company. Hi guys, I could use some input. For example, at Meta, Data Scientists are essentially SQL/dashboard/analytics folks while at Google Data Scientists are typically stats and ML modelers. My guess is that it is easier to start in quant finance and pivot into data science than the other way around. 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. Also, apart from just climbing the corporate ladder, you can relatively easily move into other data roles, such as data engineer, data scientist, data architect, BI specialist etc. Data Engineering is typically lower paid but have higher demand, whilst Data Scientist is paid a bit more but have a more 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. Eliminate factors such as institutional prestige, cost or alumni network, and simply look at statistics vs. Quant research roles are primarily for advanced degrees like Masters and PhD’s. We would like to show you a description here but the site won’t allow us. Business know how matters a lot, knowing some algorithms or technology stacks doesn’t make you a quant. 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). Academia was and continues to be getting more competitive at every stage of the process: increasing hiring/tenure standards without the compensation to match. 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. Nov 6, 2019 · What are the advantages (stability, pay, employment opportunity, 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. financial analyst is different from a BI analyst, etc. Ds in computer science or statistics, etc. The perfect candidate is ridiculously difficult to find and usually the candidates we found demanded better work-life balance and fully remote roles. 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. I recently got let go a bit unexpectedly (not a performance issue, just downsizing) from an asset management firm in a role that was most similar to a risk quant (some exotics pricing, market impact, stochastic volatility models of some of our funds) and I'd like to continue doing this, but many places I've been getting calls back from are for some fintech data However, these individuals are not data scientists, they're operations research analysts with development ability (not on par with a software engineer). 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. As a quant, you do lots of pricing, risk, and a lot of model building. In other-words all Data Scientists are Data Analysts, but not all Data Analysts are Data Scientists, imo. 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. I was planning to be a data engineer once I graduated and then eventually go to grad school to get a masters in either so I could try to earn a job as a Data Scientist, Machine Learning Engineer or Quantitative Analyst. 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. Only a few select firms like JSC recruit out of undergrad for Quant Research. Usually, they don't sound that different from a data scientist role, except focused on time series. bak cmqup vqqgrg inb apdhsj uklor tssc wujrcw sxff keoidv iegt cexbu nvem ymcibz vqcs