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Certain candidates with the right quantitative skill set and a STEM MS or PhD who can code and understand how to manipulate data to their employer’s advantage have been inspiring bidding wars between banks, buy-side firms, technology companies, large corporations and even startups.
Tyler Robinson, a principal consultant at Selby Jennings, says he sees two paths for quants in the financial services industry to earn $1m or more.
“On the sell side, the route would be to rise through the ranks to an MD level overseeing a large research or analytics group,” Robinson says.
Reaching this level takes more than pure quant skills, though. You will definitely need some broader business acumen in addition to strong leadership and managerial characteristics to help navigate the internal politics of a big bank.
“On the buy side, the most likely road to seven figures would be to work your way up through the research or strategy ranks until you have enough credibility and experience to receive a capital allocation to run your own strategies as a PM,” Robinson says. “This is a high-risk, high-reward career path, but the upside can be unlimited and far more lucrative than the sell side in the long run.
“The most successful PMs I work with are not only talented quants, but also great sales and marketing people for their funds and portfolios,” he says. “Even if your strategies perform well, if you don’t raise the capital to run them with, you probably won’t break into that seven-figure range.”
Kathy Harris, managing director of Harris Allied, says that quant traders, researchers and PMs with data-science and coding skills are the best bets to earn a million-plus in a year. The key is being a computer expert who can use math models and analyze huge datasets to create quant trading strategies that are profit centers for the firm.
“In many cases, they start on the sell side and migrate to the buy side, which is a tremendous opportunity for folks that are super talented and have a great data-driven, computer-driven trading strategy – they may earn seven-figure packages,” Harris says. “If you have the right strategy in the right place at the right time, with your compensation tied back to running a profit center and your own P&L, leading a team or a pod that is running a successful quant strategy that is generating profits for the firm, you have the opportunity to do extremely well.”
That path is starting out as a junior researcher or quant developer, then you move into an investment role where you’re running a trading-algorithm-based strategy and your bonus is tied directly into the profits that the strategy makes for the firm.
Harris says that most of the quants that she has placed recently are working in C++, C#, Java or Python.
In addition to those four programming languages, Evan Sternberg, manager of risk recruitment at Michael Page, says he is seeing demand for expertise in Matlab, SAS and especially R. He’s seeing more crossover between the two worlds of developers, who might report to the head of IT or the chief risk officer, and front-office quants, who often report to the regional head of trading.
“There’s a larger increase in demand for candidates who are more statistical and data-driven in nature with knowledge of programming languages,” Sternberg says.
At small-to-mid-sized banks, the all-in comp for quant VPs and EDs typically tops out at under $1m per year, although the bulge-bracket U.S. banks sometimes pay more than that. MDs are much more likely to earn more than a million.
Sternberg cites a front-office quant MD at a mid-sized foreign bank who makes a base salary of around $500k base plus a 100% bonus.
Senior model developers are less likely than front-office quants and traders to exceed $1m, as their comp is more evenly split between base and bonus, but they can do so if they make MD and lead a desk.
Sell-side MDs and the regional heads of model development typically earn $350k to $550k on the base, with all-in comp including the bonus ranging from $650 to $800k.
“The head of model development may make over a million at bigger intuitions like Goldman, J.P. Morgan and Citi,” Sternberg says.
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