By –Paul Clarke: Data is not sexy. Data scientists, despite largely being associated with statistics and technology, are, however. The ‘sexiest job of the 21st century’ is finally coming to banking. The problem is, though, every industry wants to recruit them.
HSBC has stuck its neck out and started the hunt for a ‘data scientist’: “Early arrivals have a great opportunity to shape the development of this core capability,” it says. Other banks, via recruitment agencies, seem to be following suit. Despite all the furor around big data, and its revenue generating possibilities, banks have so far resisted the urge to build big teams around it.
“Big data was mooted as an area of recruitment, but the CIOs and COOs put it on the back burner as regulatory pressures, cost reduction initiatives and other revenue generating projects took precedence,” said Paul Bennie, managing director of IT in finance headhunters Bennie MacLean. “Instead, they’ve turned to contractors or external consultancies, who haven’t really committed to big data initiatives.”
The Harvard Business Review deemed data scientist the ‘sexiest job of the 21st Century’, but they were really only referring to demand. The reality of the job – applied mathematics, working with unwieldy datasets, advanced computing techniques like machine learning and AI – means that it remains the domain of the quantitative nerd.
Many data professionals are already working in number-crunching roles in the City and Wall Street, but the ‘data scientist’ title – coined by two data analysts working for LinkedIn and Facebook in 2008 – encompasses a wide range of different statistical roles on a much larger scale. The likes of actuaries and quants could make the switch, but many data scientists are also moving from electrical engineering, astrophysics and neuroscience.
What’s more, banks need to decide what type of ‘data scientist’ they want to hire. According to a new book, Analyzing the Analyzers, by O’Reilly Strata, it’s not a ubiquitous role, and tends to fall into four distinct categories. ‘Data creatives’ think of themselves as artists or hackers and excel at virtualisation, ‘data developers’ focus on writing software to do the analytical and statistical tasks, ‘data researchers’ typically come from academia, while ‘data businesspeople’ are often MBAs able to apply their mathematical knowledge in a profitable way. The latter, it seems, would be more at home in banking.
HSBC wants to see knowledge of Hadoop technology – something that has recently gaining traction in banking – as well as programming languages R and Python (until recently restricted to J.P. Morgan’s tech platforms in investment banking).
Recruitment for data scientists remains in “its infancy” within the banking sector, according to Robert Grant, sales director, at banking technology recruiters Cititec. “Some banks are reacting more quickly than others, but some still don’t appear to have a strategy to manage Big Data in place, whilst others have a clearly defined plan of action which they are engaging with,” he said. “Banks typically do not react as quickly as other industries. They are like oil tankers, but then all of a sudden there will be a burst and demand will kick in. We saw this with Scala, for example.”
Data scientists are, however, more comfortable moving into the banking industry compared with other technical skill sets, notably digital technology, argues Grant, but financial sector firms are not going overboard with salaries.
Usually compensation in the US Banks is around $90K – $175K Salary and $500 to $1000 US a day for consultants, – Ted O’