Investment banks are getting wise to big data – but a demand for staff to help sift through the large volumes of information in financial markets is vastly outstripping supply.
Banks compete for scarce big data talent
Goldman Sachs, the Royal Bank of Canada, Societe Generale and Barclays are among the banks advertising big data jobs in London, while the Bank of England is also on the lookout for a data scientist to work in a new analytics division, according to its website.
Damian Sutcliffe, head of Europe, Middle East and Africa technology at Goldman Sachs, said: “People have become more aware of the value of data and the insights that can be gained from it.”
Big data strategies involve managing and processing the high volumes of complex data that banks collect and store, to make more data-led decisions across business functions including trading analytics, compliance and client management.
Sutcliffe says “data scientists are key to achieving this”.
Credit Suisse is implementing a big data solution for trade surveillance, according to a job advertisement on LinkedIn. The bank will use new software to process data feeds from as many as 80 systems, the advertisement says.
A study published last week by Thomson Reuters, which was carried out by research firm Aite Group, found that while many capital markets firms are still new to big data’s potential, data-related functions have increased in importance.
However, banks are coming up against a shortage of the required skills. The most common problem for capital markets firms on big data projects was that they lacked technical knowledge, according to the Thomson Reuters survey.
Carmine Gioia, a visiting associate professor at the Massachusetts Institute of Technology who was recruited by Saxo Bank in December to lead its data strategy, said: “You need a team of people that span mathematics, computer science, engineering, statistics and psychology.”
Sutcliffe at Goldman Sachs said: “Probably the biggest challenge is that many of the best and brightest data scientists don’t think about a career in the financial industry – instead they assume the most interesting data science problems are either in academic research, defence industry-national security, or technology companies.”
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