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Just how much statistics and numeracy are needed to work in the financial industry? While there’s no one straight answer, there are some general guidelines that potential financial professionals should keep in mind when aspiring to work at a bank, hedge fund, or anywhere else in finance.

To begin with, let’s talk about some of the basic financial math used everyday. The most important concept in finance is arguable the time value of money (TVM), which creates essentially a ratio between the present monetary value of an amount to the potential future monetary value of that same amount. The calculations to figure out TVM are basic algebra, and solving them involves little more than remembering the formulas (or, let’s be honest, Googling them) and then inputting the variables for your particular question.

The real talent that financiers need, however, are twofold. For one, they need to know which financial calculation to use for the particular financial question they are facing. Secondly, they also need to know what assumptions to make and to have solid and grounded evidence-based reasons for choosing those assumptions. A lot of TVM calculations assume a “risk-free rate” or a natural growth rate, and those are not objective numbers. Similarly, calculating NPV, IRR, and other basic financial issues requires some assumptions about the future, and those assumptions will by definition be individual, qualitative value judgments.

These are fundamental finance calculations, but not all finance is fundamental. In quantitative analysis, for instance, researchers will look for correlations between potentially millions of data points to find a pattern that can be exploited for profit. These analyses involve extremely complicated statistical models that rely on extremely complicated formulae and a high level of statistical knowledge. Quant researchers earn a lot right out of the gate for this very reason: they are extremely well educated STEM graduates who need a strong financial incentive to not work in another industry where their skills are equally useful.

Quants are relatively new, but they’ve been around (around a decade or so) for us to identify a career path for them. Quants start off earning well, but few of them rise up the ranks aggressively. And there are still, as far as I’m aware, no billionaires in finance who started off as quants and rose through the ranks. This may be due to the need for qualitative and quantitative judgments to produce alpha.

The lesson for anyone starting out is pretty straightforward: you should know enough statistics to be able to handle calculating variances, using standard deviations, and creating probability models for different financial hypotheses. But you don’t need to be a master of the math universe if you want to work on Wall Street—and being so might actually limit your career path.

If statistics are not the primary tool to rise up the ranks, what is? This largely depends on your career path. M&A analysts will probably need to be very fluent in Powerpoint, Excel, and persuasive. Sales and traders will need to have the tenacity and resolve to constantly cold call and then follow up on prospective and existing clients. Researchers will need a combination of strong analytical, mathematical, and verbal skills to do good research and write up that research in a compelling form.

Note that statistics is not the backbone of any of these positions (although elsewhere it definitely is, such as a custom derivative underwriter or a multi-asset portfolio manager). If you want to work in finance, you need to be both articulate and numerate; it’s an industry for well-rounded people.