Here’s Why Investment Firms Can’t Find Good Data Scientists

Over two days at this week’s AI and Data Science and Trading Conference in New York, one general theme became very clear: Banks, hedge funds and asset managers are desperate for good data scientists. The prevailing theory has always been that the supply just isn’t enough to meet the demand. But hiring managers across several different panel discussions suggested that wasn’t necessarily the case. The bigger issue is that the majority of master’s and PhD candidates simply aren’t skilled enough in all the areas necessary to demand big pay packages.

When asked about the biggest challenge facing the data science and machine learning community over the next five years, Anthony Ledford, Man AHL’s chief scientist, pointed to the lack of “suitable” candidates, noting that too many young people think they can just plug-and-play data mining tools and be successful. “Finding those people with the curiosity and ability to really scrutinize data and come back with something different,” is a challenge, he said, noting his firm has become extremely “fussy” about who they hire.

Sameer Gupta, head of data sourcing at Point72, said that much of what he considers “talent” in data science is passion and a curiosity to learn. Yet he fails to find those traits in many candidates with perfect resumes, 4.0 GPAs and Ivy League educations. “They know all the buzzwords, but they are rarely the people for the job,” he said. Some new recruits are also surprised or turned off when they are asked to do so-called “janitor work,” an industry term for data preparation and cleaning, according to Two Sigma’s machine learning guru Claudia Perlich.

The apparent conflicts between resume and execution is why Gupta said he’s not a fan of interviews, something echoed earlier by his colleague Matthew Granade, Point72’s chief market intelligence officer. Instead, they prefer projects and tests.

“Give them a problem to solve, throw them in the water, and if they don’t like it or can’t do it, they often don’t even come back to you,” he added. “It’s a great way to figure out who to bring on.”

The general takeaway was that firms need to look beyond target schools and GPAs to find niche talent. Roughly 80 percent of PhD-level machine learning talent works at Google and Facebook, said Sarah Hoffman, vice president of AI and machine learning research at Fidelity Investments, citing an unknown study (likely touched on in this paper). That leaves just 20 percent for everyone else, meaning they need to be more creative.

Investment firms have to get involved in more university projects and hackathons, she said. “We need to get to the point where we can say: ‘I don’t care about if you went to college, I’m going to give you data and see what you can do with it.’”

Talking the Talk

The final struggle for hiring managers is finding the right personality match. An A.I. and machine learning headhunter had previously noted that a lack of communication skills and an inability to interface with key stakeholders at investment firms has held back many potential data scientists.

One such example was given to us anonymously by a speaker at a different forum who recalled interviewing a candidate who had a strong resume for an engineer at an asset manager but who didn’t appear to have much in the way of communication skills or a willingness to engage. The interviewer tried to get the candidate to open up a bit, asking him to say something about himself that wasn’t on his résumé. After pausing for a few seconds, the candidate questioned why there would be anything that he wouldn’t put on his resume.

In an attempt to clarify, the interviewer said he just wanted to get to know him beyond his list of achievements and skills. The candidate paused again, this time for upwards of 45 seconds. He finally responded: “I don’t own an air conditioner.”

In his report, the interviewer noted that the prospect “could well be a genius, but you can never put him in front of anyone.” They decided to pass.

This article originally appeared in eFinancialCareers.

7 Responses to “Here’s Why Investment Firms Can’t Find Good Data Scientists”

  1. Venkat Peddinti

    Good Data Scientist needs Strong Market knowledge like fixed income and derivatives.
    Strong in investment banking process knowledge is less so performance might be loss.
    Lack of database complex quires and web security lacking like python scripting.
    development of cloud security and design aspects is lagging.

  2. Tom Mariner

    ““Give them a problem to solve, throw them in the water, and if they don’t like it or can’t do it, they often don’t even come back to you,” he added. “It’s a great way to figure out who to bring on.” When I got hired as a “programmer” waaaaay long ago within one day two companies, one of them IBM, did exactly that in the form of a series of tests. Aced both, got hired, and wrote the OS for our nations on-line banking system and helped invent the video game industry. Etc.

    Another interesting comment was that the advanced degree folks are not the best fit. I work now commercializing research out of universities and labs and some of them yeah, would not be good fits. But … one of our advisers heads the statistics department of a major university — smartest guy on the planet in Data Science and has used it to cut MRI exam time by 70%.

  3. James Igoe

    Mentoring anyone? To say you simply cannot find good candidates is missing an opportunity. There are talented people who maybe need presentation skills and the development of business acumen. Coaching people generate loyalty and productivity. There are things like management training programs or even education programs that are prorated to at least tie someone to the firm that paid for their education and development. If you don’t, you have the cold comfort of high standards…

  4. Nicholas Kinney

    I would consider a shot. I know the buzz words (they are mostly BS) and have 15 years of experience writing software for many different kinds of business; entertainment, construction, e-commerce, government, non-profit youth, house of corrections, beauty products. What these companies need is real talent not a piece of paper. Talent is earned through experience just like wisdom is gained by making good and bad decisions and dealing with the results. I am what I am not what you want me to be.

  5. Asset managers indicating data science/engineers ropes don’t have requisite soft skills is the pot calling the kettle black. Anyone who’s worked with research analysts/directors/portfolio managers knows many of the talented ones are far out on the spectrum and/or simply lack EQ. Let’s move past the biases, past the search for unicorns, past trying to get everything in one package, and pair folks up with others who have complimentary strengths and experiences – we could even call that approach…diversity.

    • It comes down to the hiring managers not understanding the tech so they don’t know A) How to apply it themselves and B) who to groom on their teams to perform these tasks.
      Machine learning and Data Science are more about understanding the nature of those tools, how it can be practically applied and how to collect , scrub and process/use the associated data. If the hiring manager doesn’t understand how this is used this then what the hell are they doing looking for these people? It’s the blind leading the blind.