The volume of data required and ingested by quantitative research companies is not as large as that of tech companies such as Google and Amazon. However, in today`s world, data diversity is essential for quantitative research companies to obtain insights ranging from social media to geographic and weather datasets. At G-Research, we use rigorous scientific methods, robust statistical analysis, and pattern recognition to analyze a diverse data ecosystem and gain in-depth insights from different data sets. Julien Lavigne Du Cadet, Head of Data Processing Development at G-Research, shares some of the challenges of working with such a variety of datasets. At QuantStart, we often receive email inquiries about the possibility of making a career transition to quantitative finance, especially for people who are currently in the middle of their careers. I always try to understand the term “quantitative xyz”. I`ve seen many terms used interchangeably, including “quantitative trader.” My job is to analyze data, find signals, and create mathematical models so we can do automated trading based on algorithms. I suspect that this role does not change much between accessory stores and hedge funds, because where the money comes from has nothing to do with our work. For those of you with experience, what are the real salary comparisons between working as a software developer in a FAANG-type company and a quantitative analyst/researcher in a leading quantitative store? Also, how is the general lifestyle compared (the quantum/financial world is much more stressful from what I`ve heard/seen). I know that being a quant requires a very strong background in math/computer science in addition to CS, but it`s definitely something I would work towards during my time in college. Overall, is there anything specific I should focus on for any of these career paths, or are the skills largely compatible? QR interviews will very often ask 1, often 2 will ask, some will ask 3, and depending on experience, some 4 may be requested. Trading interviews will take almost only 2, possibly with some 1 and 3 in rare cases. Quant dev can also request all four types, with varying frequency depending on the company.

SWE usually asks 3 and 4 and depending on the company (for example, some offices on the citadel) some 1 and 2. For quantitative developers, there are a variety of interview practice websites and helpful manuals. Two websites that are particularly useful for practicing interview problems are HackerRank and Leetcode. They contain a huge collection of data structures and algorithmic questions, where you often need to find not only a functional solution, but also an optimal one. For quantitative researchers, the Kaggle competition platform is a good place to gain skills in data science and machine learning. With Kaggle, you can learn practical skills in “portable” environments such as JupyterLab or R Shiny. The process of running end-to-end competitions in this man will give you hands-on experience with Python libraries such as NumPy (for manipulating digital arrays), Pandas (for panel data analysis), Scikit-Learn (for flat machine learning), and even TensorFlow or PyTorch (for deep learning). The high-end entry-level quantitative is much more lucrative than comparable FAANGs. The culture of the tech company is usually collaborative, and you work with multiple partner teams on a daily basis.

It seems to be true for some companies like HRT (heard from several independent sources), but there are companies like Citadel and Tower Research that are known to be much more difficult. So in terms of culture, it seems to be very different. Hope this helps! In addition, my mathematics is not good at all, since I taught the subject from the 4th to the 5th grade. I didn`t particularly like classes, so it`s unlikely I`ll become good at math and become one later!!! Can I still try Quant ? I`m obsessed with money with TBH, so quantitative finance is strong in my mind 🙁 Some of the good companies in this space that I heard were: Renaissance Technologies, Hudson River Trading, Jane Street, PDT, Citadel (just a few teams). Each of these places would pay much better than the best tech companies (assuming level 5 aka “senior engineer”). In terms of compensation, I got something equivalent to 2+ promotion levels at my previous location. I`ve heard from friends that other companies offer about the same compensation as tech companies. But they eventually seem to become prime ministers themselves elsewhere in order to get better pay. Modern quantitative financial interviews – researchers or developers – require considerable preparation due to the level of technology and expertise involved. It should be emphasized that it is no longer possible to “enter” a quantitative role from science or technology. Preparing for job interviews is essential.

I am on my way to university and will most likely study CS with some sort of Business/Finance/Econ minor. I`ve been coding throughout my time in high school, and while it`s obviously still early enough to show where I`ll eventually end up, like any other CS hopeful, I`d like to work at a FAANG-type company in the future. Most tech companies base their models on current data. Up-to-date data for these companies is crucial: for example, a referral system can only view last year`s Internet browsing history, because you need to optimize the current behavior. The opposite is true for quantitative research. G-Research has been storing, using and analyzing relevant data every day for more than 20 years. Our researchers create models that need to take into account different market conditions and don`t have to adapt to current data. This creates complexity as data sets change significantly over time. Whether you`re pursuing a research or development role, it`s essential to have hands-on skills in programming, software development, and modern agile project management techniques and tools. Like anything else, programming requires extensive practice, so be sure to set aside some learning time to work on coding issues.

I got a PhD in computer science from a well-known American school and went to a big tech company as a researcher/SWE. I stayed there for a few years and recently made a career change to finance. I`ve been coding since I was a kid and I`ve been in a number of coding contests, so of course I was 100% convinced that my path was in the tech industry. But I haven`t been very happy with the work lately, I`ve started looking for opportunities elsewhere. I interviewed quantitative companies, partly out of curiosity, because I knew a few friends who also did coding contests and went to quantitative companies. Now I work as a quantitative researcher at a buy-side company (sorry if I`m careless with the terms and am still learning about the industry). In school, I was always a math guy among CS people and a CS guy among math guys. So yes, this position suits me much better than my previous job.

It is also increasingly common for quantitative companies to hire directly through their own career portals. Another recruitment mechanism favoured by large quantitative funds is that of “programming competitions”, which take place every year. If you find an innovative solution for such competitions, you can be one step ahead in terms of employability. Even with strong experience in these areas, it will still be necessary to demonstrate the ability to apply these techniques to quantitative financial data sets, which are known to be non-stationary and have a low signal-to-noise ratio. Be prepared to answer interview questions on these aspects. In this article, we`ll discuss which roles might be suitable for a technical lateral entrant, how to use your current skills, and how to prepare for the types of interviews found in today`s quantitative hedge funds and modern investment banks. After working for a large tech company, is it possible to move to a quantitative research/quantitative development/quantitative business role (i.e. not pure software engineering), or are they only hiring new graduates/PhD students? But there are a ton of additional positions at FAANG and few quantitative positions.

It will be difficult to pursue a mid-level quantitative trading research role without prior experience in quantitative finance research or examples of other rigorous research being conducted in your current industry. In quantitative funding, it`s much easier to apply for jobs than to prepare for them. The most common paths in quantitative roles are either through your current network (e.g., your PhD students/research lab colleagues who have made the transition) or through dedicated quantitative finance recruiters based in major quantitative centers – New York, London, Hong Kong, and Singapore. On the positive side, many companies are now starting to hire general “data scientists” to work on alternative data (as opposed to traditional price/volume data). Data science and machine learning in Python (NumPy, Pandas and Scikit-Learn) will be valuable here. If you have a background in these technical areas, you may feel in high demand. Software developers are quite similar to the SWEs of all other technology companies. They work on the trading infrastructure, tools, backend, etc. As part of their career, all engineers must master mathematics to some degree. While physics and engineering often emphasize deterministic differential equation methods, new concepts such as uncertainty quantification introduce modern engineers to robust statistical and machine learning methods. By building on these tools and highlighting them during the interview, it will be further proof of your suitability for quantitative finance positions.