Machine Learning: The Crossroads Between Tech + Investment

Technology has been driving innovation in the financial services industry since the early nineties when the internet revolutionized investing. Allowing hedge funds and investment banks to access more and more data every year, these organizations are now turning to machine learning solutions to make smarter trading decisions based on this information. This has, in turn, led to an entirely new wave of hiring for these machine learning finance jobs.

“Today’s financial services industry would certainly be unrecognizable to financial services professionals 15 or 20 years ago,” says Rob Quatroni, a Senior Director within The Execu|Search Group’s Financial Services division. “Tasks once handled by paper and decisions made by individuals are now being automated. These new technological capabilities help keep accounts secure, improve risk management, and offer various investment strategies.”

Not only are these programs highly sophisticated, but they are actually working. For those organizations who have begun using these algorithms, they have seen better returns. Because of this, they are investing more funds into resources to support expanding machine learning operations.  “We’ve seen a lot of growth in hiring surrounding machine learning, and those who have the right background can land some very lucrative opportunities,” says Rob. “As we continue increasing our use of technology, the amount of machine learning finance jobs will only continue to grow.”

A shifting skillset

Because trading is shifting further toward data and algorithms, the makeup of front office roles has changed significantly. For example, financial institutions are now hiring Quantitative Analysts, Data Scientists, and Machine Learning Scientists. “These days, firms are looking for people who understand these complicated algorithms to sit at the trading desk,” says Rob. “In this sense, we’re really at a crossroads between tech and investment. As a result, many of these companies are expanding their search to Silicon Valley to source talent.”

With finance professionals no longer just competing with each other for these machine learning finance jobs, those without a STEM background might want to consider advanced degrees in this area. For example, an engineering major will give you more exposure to that combination of high level mathematics and programming languages that are now at play on the trading floor.

However, it may not be in everyone’s reach to complete an advanced degree. If you are looking to gain in-demand skills without such a commitment, learning the most common system skills used in machine learning is still incredibly valuable. “Taking courses to learn MATLAB, R, or Python can still open several doors for you as well as give you a significant edge up on your competition,” advises Rob.

Additionally, STEM professionals who are looking for a lucrative career change may find what they’re looking for in these machine learning finance jobs. “If you have the STEM background and are tired of Silicon Valley, a hedge fund or investment bank could be a valuable career move,” says Rob. “These firms are hungry for talent in this field, and are willing to pay top dollar as a result.”