Revolut's employees are known across fintech for being some of the brightest, but how does the banking fintech's tech stack facilitate their development? Speaking at the Global Fintech Fest this month, Revolut's CTO and co-founder Vlad Yatsenko offered a few insights.
Yatsenko said Revolut is an "ecosystem of over a thousand applications," where 99% of technology is built in house.
That many applications be a problem when it comes to expansion. However, Yatsenko says Revolut has a "single framework" that the almost 2,000 people working in product for the fintech use when designing, building, releasing and collecting data on a product. Product staff are allowed great flexibility, provided they stay within the framework.
Data scientists at Revolut also have to stay within the framework. Yatsenko says data science "is one of the key roles we’re hiring for." Data science is not mature, says Yatsenko: "People come in with very different ideas" over the right way to do something.
So how does Revolut reign its data scientists in? It stops them coding. "It’s very well known that data scientists are doing software engineering 90% of the time," Yatsenko says. "That’s a big problem." A pressing issue with data science jobs in finance is that talented modellers with subpar coding skills are not very attractive to hire... unless, it seems, you're Revolut.
Revolut has built an in-house "platform for data scientists to work on their models." The intention is for them to spend 80% of their time working on models rather than getting bogged down by figuring out how to deploy it, run it, train it or connect it to data sources.
If you're excited to work with a system like that, you may have to wait. There are only four data analyst roles open at the moment; three of these are in mainland Europe, while one is in its Mexico office.
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