In discussing some of the issues facing investment firms that are hungry for data scientists and machine learning experts, several executives speaking at this week’s AI and Data Science and Trading Conference pointed to the lack of gender diversity in the candidate pool.
Sheedsa Ali, portfolio manager and quant specialist at Pinebridge Investments, estimated that more than 95% of data science candidates that walk through the door are men, and that if she doesn’t intentionally source to be more diverse, the pipeline of female candidates would essentially be barren.
“With the newness and the speed of data science, diversity has become an afterthought,” she said. “People are competing against other industries and are saying ‘I just need someone,’ and don’t pay attention [to diversity] at the time.” Ali noted that gender ratios are even more skewed in data science than they are in quant investing.
The biggest issue seems to be the lack of diversity in the candidate pool, rather than deliberate efforts to only hire men. Nitish Maini, a PM and head of the virtual research center at WorldQuant, said the firm recently ran a competition to try to identify and connect with more young quantitative finance students. Roughly 11k people from 80 countries participated and the top three winning teams had seven women out of the 11 total members.
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, said that promoting diversity is not only a moral purpose, but also a business purpose. “We have a fiduciary obligation to broaden the search. The more diverse candidates, the better the [financial] outcomes,” he said. Considering the amount of churn in data science, “diversity is a key tenant in retaining talent,” Furstenberg added.
It seems like the data science community has a long way to go, however. Chopping down that 95% number will take some work.
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