A former sell-side and buy-side quant recently bemoaned in an op-ed on our site that his brethren rarely see the seven-figure pay packages that traditional traders and portfolio managers can sometimes earn. His thinking was that engineers on investment teams are often devalued due to old-fashioned thinking that mislabels them as dispensable non-revenue generators. The people who hire machine learning and AI-focused data scientists don’t agree. Million-dollar paydays are indeed attainable, they say. The only problem is that the vast majority of masters and PhD-level engineers who work at hedge funds and on investing teams tend not to have the required skillset.
Speaking at the AI and Data Science and Trading Conference in New York, a panel of hiring managers and recruiters from the asset management sector acknowledged the high-level of competition over data scientists, but added that the supply has slowly crept up to meet the demand. It's just that there's a huge gap between top recruits and all the rest.
A few years ago, “coding and creating signals and APIs were the ultimate. Now, those are merely the table stakes [just to be considered],” said Dan Furstenberg, global head of hedge fund distribution at Jefferies who has advised on the buildout of over 100 data science teams for investment managers. “Where asset managers have struggled is with the upper quartile of responsibility.”
The “holy grail,” as he calls it, is a data scientist who can bridge the gap between the analytics team and the C-suite – someone who can sit in investment meetings and understand the portfolio construction process while also “knowing the plumbing,” he said. In short, if you want to get paid, you’ll need all the technical skills as well as a strong understanding of capital markets.
“That pool of candidates gets limited very, very quickly,” added Richard Pook, an executive search consultant at Dore Partnership who specializes in machine learning and AI. Pook said that most candidates lack the communications skills as well as the contextual knowledge of investment principles.
However, compensation for those data scientists who develop the “holy grail” of technical skills, investment knowledge and the ability to communicate with stakeholders “has ballooned over $1 million” annually, Pook said. On the other side of the coin, those who just meet the table stakes of a technical skillset and a master’s degree are taking home pay packages one-fifth that size: usually between $150k-$200k, according to Pook.
It seems talking-the-talk is just as important as walking-the-walk when it comes to getting paid as a buy-side quant or data scientist.
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