How Bloomberg is using machine learning and data science to keep users hooked to its terminals

By Scott Carey

"What is much more successful is to use object recognition techniques on those tables," Mann explained. "So it will recognise the boundary of the table, overlay with pick out columns and rows from that table into our database." This means higher accuracy and speed.

Next, Mann wants to use techniques like computer vision and natural language processing to improve the breadth of financial information available through the terminal. The aim is to allow users to increasingly make queries on the terminal using natural language instead of specialised commands.

"A lot of financial data is numbers, but a lot of the things that happen in the world that are pertinent to finance are expressed in language, either news stories we generate or aggregate, or press releases or documents the companies put out themselves, or even statement by officials," Mann said. "All of that has a dramatic and fast change on the market.

"So the bulk of the data science and machine learning work we do is language processing, applying structure on top of it."



Mann believes that Bloomberg has got much better at hiring data scientists over the years, as it has grown to understand which people the organisation needs - mainly computer science PhDs, if you were wondering. "I don't want to come off as too braggadocious, but we have got better at [hiring]. We spend a lot of energy on it," he said.

"We understand what we want and look for and over the past year we have significantly increased the quality of applicants," Mann explained. "They were always very good but the change has been we are able to hire people with a skill mix which is closer to what we need, cutting down on the training they need. I think we understand more of the people we need and the places we need to go and the universities they are coming out of."

Essentially Bloomberg has steadily shifted away from statisticians and more towards quantitative programmers for any data science that occurs within its walls.

Now Mann is taking this strategy a step further. "We used to have the idea that each of these quantitative programmers had to be full stack," he explained. "So take the data, clean it, structure it, support infrastructure, build a machine learning model, deploy it, babysit it and do fixes."

Now he wants smaller teams of specialists working on projects. For example, a data engineer, data scientist and production engineer working on a specific product within the terminal.

He recognises that the industry is changing so fast that close ties to academia are integral to stay up to date with the latest technology trends.

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