Why BNY Mellon didn’t hire ‘the perfect data scientist’ on paper
We talk to Bank of New York (BNY) Mellon‘s head of digital, Roman Regelman, who explains why it’s not just about PhDs, technical skills and domain experience when it comes to hiring a data scientist.
It’s common knowledge that data scientists are a rare breed, but what is not often contemplated is that a business could turn down someone with the full repertoire of data science skills. This is exactly what happened at BNY Mellon, according to Regelman.
“I interviewed someone who was a data scientist and a full stack developer, they had everything at once, and yet I knew that this person could not be useful for my business leaders because there was a communication gap,” he says.
That communication gap is not just about domain experience however – as many other CIOs and CTOs may have alluded to – it’s actually about understanding and appreciating how BNY Mellon operates. The ‘appreciating’ point being even more pertinent, as many data scientists may have a bit of arrogance about themselves, believing they’re the ones bringing expertise to the business without realising that the business has other specialists and requirements.
“If someone is a great data scientist, are they willing to work in our environment for our clients? We work in a regulated industry where our data is highly secured and protected, and our clients expect a certain service, a level of precision and that introduces some constraints – or opportunities – depending on the way they look at it,” says Regelman.
He maintains that even great data scientists need to be aware of their surroundings.
“All the people that bring great skills, they’re willing to push the envelope, they’re willing to challenge the norms, but they also understand the environment and they need to be great team leaders,” says Regelman.
“But most importantly of all, they need to take into account that we have a great machine learning person, and great specialists across the board who are working together to create amazing results.
“Individuals that have three PhDs but aren’t willing to work with other people, or really care for the client service, just wouldn’t be able to work here – that doesn’t mean they won’t be successful elsewhere, but not for us,” he adds, stating that integrity, trust and client services are part of BNY Mellon’s core values.
This same principle is used by BNY Mellon when it comes to hiring people for their innovation centres.
“We look for people that work well with others and leverage someone else’s expertise. A machine learning expert might know little about finance, but as long as they can work with a specialist in that area, that’s the main thing,” says Regelman.
“Some people think that you can launch an innovation centre and hire some people with PhDs and let them loose – and it might work for the first few months because they create something new, using data and doing cool things, but in reality it doesn’t change the way the business works – we have a lot of innovative ideas but we need scalable solutions that actually work all the time,” he adds.
Despite the dearth in data science specialists, BNY Mellon would prefer to wait to hire a skilled professional that is happy to work under the restrictions that are normal at a global banking giant, and that is happy to be a team player, rather than recruiting a fantastic candidate on paper.
By Sooraj Shah, staff writer, FinTech Futures