How can Data Science be as fulfilling at a Hedge Fund as it is at a Tech firm?
17 July 2018
Everybody has heard of data science in one form or another and the growing exposure to the world of data science is highlighted in how prevalent data science is becoming across all industries and ways of life (even if you’re a part of the minority that hasn’t heard about it).
This is echoed by the fact that the number of data scientist roles available in America has grown 650% since 2012 according to Forbes (excluding ml engineers, analysts etc) and is tipped to grow another 28% by 2020.
When you think of data science, it wouldn’t be wrong to assume that most people would think of the technology sector and its big named constituents such as Google, Facebook, and Amazon. However, 19% of all data science vacancies in the USA lie within the Financial Services. Yes, big name banks and big-name insurers take up a chunk of this number, but also within that number and quickly taking over a lot of the demand is hedge funds! This blog aims to give the unsuspecting data science gurus an insider view on why they should not rule out a move to the hedge fund space in their next move and to show that working at a hedge fund is not all that too dissimilar to working in tech.
When speaking to data scientists contemplating a move to a hedge fund, alongside the usual’s such as culture and salary, two things regularly come up as key drivers; the complexity of the work and how big of an impact will their work have on the company in question. Hedge funds are a rarity in the fact that they can provide one of the most complex working environments alongside great responsibility and the instantaneous measurable impact that is felt company wide. As a data scientist, you can expect to take large, complex and messy data sets and be tasked with drawing signals/insights out of it to ultimately provide investment recommendations. Working in the financial markets also means that you get, for the most part, unbiased feedback. There is no blaming the hypothesis, the analysis or the data (which happens across industries if the conclusion doesn’t fit the predetermined answer).
Methods used are generally uniform across industry, just applied using data different types of data in different quantities in different domains and there is a time and place for them all (check out my colleague Alex’s blog to find out more). Different funds have a different approach and view of what generally is the best approach. Whether you are a core machine learning scientist or a data scientist that is at home with building and applying statistical models or running regression models, there is a fund that will value what you can bring as a data scientist.
Also, circling back to the usual questions around culture and salary, hedge funds of today are built to mirror/replicate tech companies, in order to attract the best tech candidates and to provide a culture in which they would like to work. The days of the stereotypical “boiler room” funds are no more, rather today they are more akin with the Facebook’s and Google’s of the professional world. Of course, nobody works for free and as a data scientist at a hedge fund, you for sure will not be working for free. Packages of course depend on experience and skill set but are normally all cash and can range massively from the mid-low 6 figures right up to and beyond 7 figures for experienced scientists and data-driven decision makers.
I hope this has dispelled some of the myths around data science at hedge funds and encouraged those tentative technology data science stars to strongly consider a hedge fund for their next move (you won’t regret it). As a data scientist, the mission never changes, the key thing to remember is that whatever industry or setting, data science always comes back to the basics of finding patterns and deriving value from those patterns.
If you have any questions or are a data scientist looking for a next role or a data-driven company looking for your next data scientist, then please get in touch on email@example.com LinkedIn - Sam Boundford