“In the future, everyone will be a data scientist.” So asserts Bloomberg opinion columnist Matt Levine (in a 2018 op-ed).
That’s still a minority opinion, although things are changing. According to the current popular view, data science is exclusively a tech enterprise; data scientists are professionals who work at tech firms like Google or Apple. Data crunchers who work at finance companies, in contrast, are thought of as quantitative analysts. Levine argues, however, that there is no hard-and-fast dividing line between quants and data scientists, and that eventually, the baseline expectation for quantitative analysts will be fundamentally the same as those for data scientists.
We’re not quite there yet, but we’re getting closer every day. In every Reddit or Quora thread about the difference between quantitative analysts and data scientists, some commenters argue that where someone works determines whether they’re a quant or a data scientist. Others offer that they’ve been able to apply for positions with both titles given their skill sets. Others still insist that the two roles differ in critical ways. What’s certain is that the quantitative analyst vs. data scientist question is one that provokes significant online debate.
In this article, we compare quantitative analyst vs. data scientist by looking at what they do, how they’re trained, what they work on, and how well they’re paid. We’ll cover:
Quantitative analysts use complex math and modeling (aka quantitative methods) to help financial firms price and trade securities and to make trading more efficient by improving protocols and strategies. A quant’s findings can also help companies make less risky decisions or manage unavoidable risks. Quants work at:
There are three types of quants:
Front-office quants work out on the floor with traders and salespeople, developing new pricing and trading tools. Financial analysts who generate revenue are front-office quants. Back-office quants conduct research and create new trading strategies. Financial analysts who provide support research to the front-office are back-office quants. Mid-office quants assess assets and markets for risk.
Are quants data scientists? The answer depends on whom you ask. Many people assert that quants are specialized data science professionals and that some of the most brilliant data scientists are, in fact, quants.
Data science professionals can use their knowledge and skills in many ways and in almost every industry. You might specialize in business intelligence or robotics or healthcare informatics. There are almost too many options.
90 percent of data scientists hold master’s degrees, and 47 percent hold doctoral degrees. ( )
The Bureau of Labor Statistics sets median data scientist annual pay at just over $100,000. Top-paying sectors include ( ):
- Computer and peripheral equipment manufacturing ($148,290)
- Semiconductor and other electronic equipment manufacturing ($142,150)
- Specialized information services ($139,600)
- Data processing, hosting, and related services ($126,160)
- Accounting, tax preparation, bookkeeping, payroll services ($124,440)
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Data scientists are professionals who undertake data mining with powerful tools to find trends and answer questions for businesses, researchers, nonprofit organizations, academic institutions, and governments. The applications of data science are useful across industries because data can be leveraged to solve abstract problems or predict future events.
In finance, it can be used to set pricing and make trading more efficient—sound familiar? In pharmaceutical research, it can be used to discover which populations are most responsive to a drug. Governments can use data science to determine how initiatives like Universal Basic Income will help people (or whether they will help them at all). A nonprofit might ask a data scientist to help determine how a problem affects different populations at different economic levels.
Given clear parameters and plenty of data, there are very few questions that can’t be answered by professionals in a data scientist role.
The short answer is yes. Both quantitative analysts and data scientists gain knowledge and insights from data. They’re both capable of building tools to analyze large amounts of data. Most of the time, quants work in finance companies and data scientists work everywhere else, but you’ll find data scientists working at finance firms and quantitative analysts working at tech companies and IT firms. Sometimes they’re doing the exact same work. In other cases, data scientists and quants will work at the same firms but do different things: the data scientists acquire and scrub the unstructured data sets while the quantitative analysts analyze it and use it to create tools.
These days, the line between quantitative analysts and data scientists just isn’t that clear. Some people define quants as mathematical thinkers and data scientists as mathematical programmers, where data scientists are next-generation quants doing the same job differently. That’s Aaron Brown’s take. In a Quora thread, he opines: “I have little doubt that computers will increasingly replace human decision-making in all fields, certainly in finance. So, either existing quants will up their computer game, or new types of quants will replace them.”
Not usually, but sometimes. The majority of data scientists have advanced degrees—master’s degrees and doctoral degrees—and most earn their bachelor’s degrees in computer science, math, statistics, or engineering. Some data scientists get their start in engineering, physics, and biology, and later enroll in data analytics, data engineering, applied statistics, and business intelligence master’s degree programs. Harvard University, Columbia University, and Tufts University have all developed data science master’s programs, and there are related degree programs at:
Most data scientists are at least partially self-taught because new data science techniques and tools emerge all the time. By the time a data scientist graduates from a data science program, the data science landscape may have changed dramatically.
Most quantitative analysts also have advanced degrees, but these tend to be doctorates in mathematics, economics, finance, or statistics. A Master’s in Quantitative Finance (MSQF or MScQF) can help aspiring quants get a foot in the door. Some of the best master’s in quantitative finance programs can be found at:
There’s no set educational path for either data scientists or quants, but in general, both quantitative analysts and data scientists need to be excellent at math and statistics. Traditionally, data scientists needed programming skills (which is still true) and more technical skills, while quantitative analysts could get by without them (which is changing). Data science programs usually touch on:
Quants in finance programs study stochastic optimization, PDEs, Monte Carlo methods, and numerical methods—along with asset management, risk management, predictive analytics, and other topics specific to finance.
Most employers in finance look for quants (and data scientists) with PhDs or other doctorates, whereas tech companies may hire undergrads fresh out of data science or computer science bachelor’s degree programs.
Some people claim that while quants can make $500,000 or more with bonuses, data scientists have no chance at that kind of salary unless they are AI researchers. On the other hand, Adam Zoia, the founder and CEO of recruiting company Glocap, claims that data scientists in finance are closing that gap. “If a candidate’s got a Ph.D. in the right subject from Stanford or Yale and internships at Apple and Google or another Silicon Valley giant, they’ll get an offer in the $350,000 range right off the bat,” he said.
The reality is that no one is winning the quantitative analyst vs. data scientist wars when it comes to salary. The typical mid-career data scientist salary is $123,000 while the typical mid-career quantitative analyst makes about $139,000. Quants definitely make more money, but not hundreds of thousands of dollars more, and it may be that data scientist salaries will catch up sooner rather than later.
Data scientists and quantitative analysts both manipulate information, but there are some critical differences between these careers.
Because quants are more likely to work at big finance firms (for now, anyway), they tend to be put through the wringer when interviewing. One Quora commenter said that the quant interviews at finance companies are “extremely tough and stress-inducing—it’s as if at one point it turned into an IQ test… where the questions were constructing strategies in real-time of inefficiencies of any arbitrary situation.” The data scientist interviews at tech companies, on the other hand, are “more like a casual conversation, mainly testing if I really understood the product, basic statistics/probability and hypothesis testing, and a few questions for why I was passionate about the product.”
Data scientists have more career flexibility than quants. Their skills tend to have broader applications, which means they can move between industries easily. Quantitative analysts may only be qualified to work in finance, depending on their training.
It’s worth noting that this article may become obsolete in the future as financial firms increasingly turn to Big Data when making decisions. Quants and data scientists are already pretty similar. Chances are that it won’t be long before they’re practically the same thing.
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