What will be your focus areas in the next 12 months? How do those focus areas support your company's overall business goals?
Bloomberg's technology drives the world's financial markets, so we are constantly investing in data science and machine learning. Data science is important as there are many problems that will benefit from machine learning.
When the Bloomberg Terminal was first introduced, it changed the playing field and offered greater transparency to financial markets. We were able to help our buy-side clients calculate fair values for intraday municipal bonds that they couldn't do before. Machine learning is the only way to process and summarise the huge volume of new data coming in each day - 100 billion market ticks, 1.5 million articles from 125,000+ curated news sources - to help our customers glean insights and make more informed decisions about their business and financial strategies. Even for things that we have traditionally done, we have been able to do them faster, cheaper.
In the next 12 months, we will focus on two areas. One is in building out our internal data science platform to make it easier for our teams to build new products and do exploration. Our data scientists will work with our various product teams to build out their particular purpose areas based on their overall business goals.
The second area is to continue training our software engineers and financial mathematicians in machine learning. We have more than 5,000 technologists at Bloomberg who define, architect, build and deploy systems to fulfill the needs of financial participants globally. This includes more than 200 engineers who are working on data science problems and nearly 100 data science experts.
Our goal is to increase the speed and velocity with which we can deliver products. To serve our clients better and faster, we need to increase the number of people who can build these products and, at the same time, increase their efficiency.
As AI advances rapidly, how do you forsee it affecting a data scientist's role in future?
In the future, data science and machine learning will be central in a lot of ways to how businesses create value. We are in transition towards a data science point of view in the world of finance and investing. Increasingly, there is a need for data scientists to take roles in management to effect overall product direction and to integrate these ways of capturing value into an organisation's business strategy.
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