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How asset managers are competing for the hottest skill-set on the buy-side

The asset management industry is changing, and some people won't make the cut.

The underperformance of asset managers’ actively managed strategies taken with the rise of new technologies has shaped asset management recruitment this year and will continue to do so, moving data scientists and artificial intelligence and machine learning specialists to the top of the priority list for long-only asset management and fundamental hedge fund firms, not just quantitative hedge funds.

In March, BlackRock fired more than 30 people in its active equities group, including five fundamental portfolio managers, and moved $6bn of the $201bn run by traditional stock pickers into low-cost funds with a quantitative element, according to Bloomberg. This year, its recruitment focus has reportedly been on quant researchers and candidates conversant with data analytics and with specific technological skills, including Rajesh Nagella, who last month joined BlackRock in New York as a managing director after leaving Citigroup, where he was the head of algorithm products for the U.S. and head of its Americas execution platform.

What skills are asset managers looking for now? The answer, quite simply, is data science, but this doesn't mean investment staff are being cut across the board.

“We’re seeing a really big push for data scientists and quant researchers, and not just across the quant [hedge fund] side – they’re being added to the fundamental research teams as well,” says Reshma Ketkar, the head of the asset management recruiting practice at Glocap Search. “I’m working with a big fundamental investor that has traditionally analyzed management teams and looked through financial documents to look at traditional metrics such as cash flow and balance sheets. They are now hiring a data scientist to cull through big data for insights to supplement the fundamental investment process."

The recruiting focus on the buy side has changed with these new technologies and functions, according to Steven Gold, a partner at recruiting firm Green Key Resources.

“We are starting to see an increase in machine learning and AI and big data analytics roles, data scientists and data analysts,” he says. “We have not seen requests for blockchain; however, that is the next thing on the horizon.”

Nearly all buy-side firms of scale are hiring in data analytics to enhance investing, marketing and operations, according to Chad Astmann, the co-head and global sector leader and asset management and alternative investments at recruiting firm Korn Ferry. Not as many have stepped into machine learning or blockchain unless it is core to their investment strategy.

“Hiring in data science and analytics has greatly increased and the profile of hiring outside of financial services has been expanded into healthcare, consumer and technology, among other industries,” he says.

Pulling talent from Silicon Valley

However, fundamental managers aren't always competing with quant hedge funds for talent; sometimes it comes from tech firms or data providers.

“Data science is a newer role, so we’re pulling from an industry that has these roles: tech, for example, alternative data providers, not always quant funds, so we’re not pulling like from like – we’re pulling from something that is a little bit different, but requires a similar skill set,” Ketkar says. “Data scientists are providing a supplemental investment function – it’s not the same thing as an algorithmic quant trader.”

Depending on how the role is structured, sometimes a data scientist is on the investment team, but sometimes it is an operational or IT hire.

“Data scientists are not making trades – their role is to analyze big data to supplement the work of investment teams, which retain discretion over portfolio construction,” Ketkar says. “They’re tasked with providing new information that the PM doesn’t have.

“It depends on how people want to apply their skill set – what are the motivations of the candidate?” she says. “If they want to provide algorithmic trading recommendations, then they’re likely not the right person for the data-science role – they’d be better as a quant researcher.”

Photo credit: Gearstd/GettyImages

AUTHORDan Butcher US Editor

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