If you want a big data job at a bank, you’d better bone up on your derivatives and counterparties. While most industries battle for a limited supply of data scientists, investment banks continue to demand knowledge of the sector in conjunction with core data skills.
“It’s very much a cultural thing,” said Rob Toguri, partner and head of enterprise intelligence at Ernst & Young. “Any of our consultants working in investment banks need an understanding of the business, not just big data skills. However, investment banks are at the front edge of technology when it comes to harnessing big data – they’re good at understanding real time trading positions and position analytics.”
Ernst & Young is investing in big data in a big way in the UK. It has plans to double headcount in its division to around 700 people, partly in response to demand from financial services organisations, suggesting that the large consultants are taking advantage of both regulatory pressure on financial institutions to manage data more effectively and a desire for banks to use big data for competitive advantage.
While consultants are hiring aggressively for their big data teams, financial institutions themselves are taking a more measured approach. Firms are only just starting to recruit for data scientist positions – quantitative hedge funds like Winton Capital Management, as well as investment banks such as HSBC have recently been hiring for the role.
However, while banking relies on industry knowledge for its new recruits into big data, other firms are being increasingly flexible with who they take on, argues Toguri. “We’re increasingly looking towards graduates with a meteorological background,” he said. “They’re used to modelling large amounts of data on high end technology, and could easily apply these skills to capital markets. It’s not about what the data is used for; the problem is analysing in a way that can be easily consumed by the business.”
Guy Harrison, executive director R&D information management at Dell, believes that finance, along with “media, online retail, healthcare, insurance”, will be one of the key industries recruiting data scientists going forward.
In order to work in big data you need: “Programming – at least sufficient to use things like Apache PIG, Hive, SQL as well as knowledge of statistical analysis, visualization and machine learning algorithms,” he said. “Finally, they need to understand the business context driving the intention and value proposition for the project.”
Such a “unique combination of skills” means that there might never be enough data scientists, he argues, while Toguri believes that the financial sector is not always the first place big data talent looks to work and could struggle to attract top candidates.
Toguri adds that EY will be competing in the labour market against the banks for big data talent, and that the types of people they’re looking to recruit are typically in “a lower age segment”. “They will have grown up with device experience, particularly around mobile, and they’ll be used to working within the Agile development approach, as well as understanding the concepts behind data virtualisation.”
Data scientists working in banking tend to earn up to £70k, according to recruiters.