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A generation ago, the career path for Wall Street was simple: go to a target school, study something, get good grades, get a job in investment banking, and then learn finance as you go and find your niche. While this is still how Goldman Sachs operates, the much more innovative and less stodgy parts of Wall Street (including even parts of Goldman) are looking for a very different way to attract talent: get the data wizards.

The reason for this is simple: there is a lot of data available, and some of it is predictive of investment returns. The problem here is identifying the data that has a signal (instead of the noise) and identifying the methodology of using that data to accurately predict market changes that results in a profit.

For that reason, data analysis is becoming an increasingly valued skill set, and data analysis is a very specific thing. Specifically, it’s a way of using mathematical tools to find patterns in aggregated data. Those tools can be statistical or arithmetic, and the data can be as simple as having a handful of variables or as complex as having hundreds. The key is knowing which tool in the data analysis toolbox to use for the data at hand.

As a result, aspiring financiers are quickly finding that the kind of data analysis that is done in specific fields has tremendous financial implications. Linguistics who find patterns in language changes, for instance, can apply those approaches to financial data to find strong returns. Astrophysicists who analyze the momentum of exoplanets to calculate their size and location can also find those approaches useful for certain financial questions.

The financial headhunter has a new problem and opportunity today: there are a lot of different fields that have relevant skill sets and unique perspectives to crunching data to boost returns. Likewise, the job applicant has a new opportunity: to effectively communicate their experience analyzing data and demonstrating how that experience can translate into financial analysis.

It’s an exciting time to enter finance because of the new and interest approaches to analyzing markets that never existed before. And that’s why entrants to finance need to spend more time thinking not only about the nitty gritty of finance, but also about the unique tools they can bring to analyzing data that are relatively rare in the financial sector.