Secretly, the two most-hated words for internal recruiters at big banks are “data scientists.” The problem is two-fold. One, investment banks have a growing need for people who can mine and analyze big data in every business unit, from sales and trading to consumer banking to surveillance and IT security. In fact, even human resource departments at banks are now employing data scientists. Exacerbating the recruiting headache is the fact that Wall Street is not only competing against tech giants like Facebook and Google, but also burgeoning startups whose business models are completely informed by consumer analytics. The result is that a lot of money is being thrown around to hire top people.
A former internal recruiter at a tier-1 bank who now works at a New York-area hedge fund said that data scientists are the most frustrating roles to work on because of the steep competition, the need to buddy up with small PhD program directors and the fact that new hires, including senior-level managers, are often poached before they get their feet wet. She also noted that data scientists coming through top postgraduate programs know their worth and negotiate compensation aggressively. “I’ve never seen a group of people – including extremely green post-grads – talk so openly and be some demanding about comp,” she said. “Frankly, I think most of them are overpaid.”
Recruiters say that PhD-level candidates with coding and programming experience through bootcamps and hackathons can now earn a base salary north of $150k from banks. The average Facebook data scientist is said to make just shy of $200k. At the beginning of the year, while ranking “data scientist” as the best job in America, Glassdoor said the median base salary for the title was $110k based on last year’s figures. By May of 2018, average base pay for data scientists in the U.S. crept up to $120k, according to a screenshot taken at the time. Less than six months later, that number now sits at $140k.
Competing with Silicon Valley
It doesn't help that everyone who is anyone wants a data scientist now. Uber, Airbnb, Homeaway, Google and Facebook to name but a few are all hiring in Silicon Valley, with many line managers bypassing HR and trying to reach candidates directly.
Data scientists in banks are doing the same. In July, a VP-level data scientist at Goldman Sachs published a “We Are Hiring!” post on LinkedIn and told anyone who was interested to reach out to him directly, rather than applying to the actual job posting for surveillance analytics engineers (a posting that is still active today).
Apoorv Saxena, J.P. Morgan’s new head of artificial intelligence and machine-learning services, posted a photo of himself with his first new hire, former Facebook big data engineer Yang Wang, less than two weeks after starting himself. The photo of the two smiling and pointing at each other under the J.P. Morgan crest included the caption: “We are hiring!” Yang’s personal profile says the same thing, asking people to contact him directly if interested in joining.
“You’d never see this style [of recruiting at investment banks] just a few years ago,” said one New York headhunter who banks pay to do retained searches for senior tech talent. “Hiring managers have bypassed human resources forever, but it was never so brazen,” she said. “But I don’t blame them whatsoever, and I don’t think HR minds the helps.” She noted that the big data community has enough options that they typically rely on referrals and networking to find the best opportunities, even with recent graduates.
To get a window into the current level of talent poaching, eight of the top 20 data scientists in finance whom we identified in March of last year have left for other roles.
Some of the bigger names to move include Angus Lund, the former global head of alternative data analysis at Morgan Stanley who was named partner at hedge fund AKO Capital in September; Graham Giller, former head of data science research at J.P. Morgan who took a similar role at Deutsche Bank in March; and Afsheen Afshar, ex-chief data scientist at J.P. Morgan's corporate and investment bank turned chief AI officer at private equity fund Cerberus Capital Management. David Loaiza, chief data scientist at Point72, recently left the hedge fund, though his next stop remains unclear.
You can see why HR and hiring managers are pulling their hair out. But it’s certainly a good time to be a data scientist, so much so that people are adding the title to their resume despite not actually doing the job.
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