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Data Science at Bloomberg: What CTO Shawn Edwards Revealed

 July 9  | 0 Comments

In a recent interview, Bloomberg’s CTO Shawn Edwards spoke about how his company is leveraging the power of big data and data science to innovate new products for their customers.

Shawn, who believes he has a great job, is head of a small bunch of researchers responsible for setting the direction of technology his firm takes. His team builds proof of concepts that determine the feasibility of the ideas they come up with. Shawn and his researchers collaborate with various teams including the sales and engineering teams to come up with new ideas, architectures and models. They also collaborate with partners outside the firm like academic institutions, the open-source community and different IT vendors.

Two Approaches at Bloomberg

The CTO team at Bloomberg watches the different innovations of digital transformation across the globe closely to put interesting data-led ideas into practice and find creative solutions to their business problems. This part of the job is exploratory, where they find something interesting and try to implement it in their business milieu. But the approach that they generally take goes the other way around. They decide on the problems that they want to address and try to identify the most effective ways to solve them.

Gaining Edge in Financial Market Place

Shawn’s team spends a large amount of their time to innovate and create new products that fit the financial market place, which is transforming. Investing is becoming a systematic process that is relying more on models and algorithms, and Shawn and his team try to facilitate this change using emerging technologies.

Together, they’re making a platform that people can use to analyse more data than they have ever done before. They’re also reinventing the way they store data and deliver it to their customers. Shawn thinks being able to raise cross-domain queries and sift through data on millions of bonds is exciting. There’s also potential in using information about people like their trading history, for instance, to provide game-changing services.

Role of Machine Learning

Machine learning is at the heart of these ambitions as Shawn thinks artificial intelligence can benefit his clients like trading firms to try new models of data or test hypotheses regarding market prices. His customers can also do the same using large volumes of data implement similar models. Shawn says his team wants to make artificial intelligence easy to use for his customers so that they can leverage benefits.

Acquiring Data Science Capabilities

Whether his team can achieve all these ambitions depends on capabilities of his team. The demand for data scientists is high, while supply short. It is, therefore, difficult to attract the right talent. Bloomberg has initiatives in place that gives them access to next-generation of data science professionals. They provide grants and run a fellowship programme that covers tuition and includes funds for PhD research up to a year. The PhD student who benefits from the fellowship programme then interns at Bloomberg to gain practical experience. Bloomberg, on the other hand, benefits from the latest theoretical knowledge that the PhD student brings to the table.

Apart from grants, the CTO team makes their presence felt at top-tier institutions through on-campus recruitment. Bloomberg offers students of these institutions the opportunity to have an impact on the business and in the industry. This helps them attract talent.

They also attend business and data science conferences to stay up to date with the latest trends and continuously interact with leading professionals. The challenge of acquiring the right data science professionals is difficult. Nonetheless, Shawn and Bloomberg are not faced by the challenge.

Upskilling Existing Talent

Upskilling existing employees is one strategy Bloomberg is adopting. One upskilling project made use of a prominent data scientist in Bloomberg’s ranks – David Rosenberg, who teaches machine learning at the Center for Data Science at NYU.

Rosenberg conducted classes personally in London and New York. In 30 sessions, he created a path for engineers and other professionals with a strong academic background to pick up machine learning so that they can use it to benefit Bloomberg. These sessions are now online for any of Bloomberg’s employees to use them. And, the results are already showing. Shawn thinks its great that an internal asset with extensive domain knowledge about a niche financial market can put it together with machine learning to find new business solutions.

Key Takeaways

Emerging technologies are changing the financial market landscape. Bloomberg has two approaches to deal with this change. One is to track the trends in digital transformation across the globe and implement them in their business operations. The other approach identifies problems they want to fix and narrows in on the right solutions.

Machine learning and artificial intelligence are at the heart of the changing financial market. Investing is becoming increasingly systematic. It is relying highly on models and algorithms. Bloomberg’s goal is to make it easy for their customers to use emerging technologies to access more data and make better investments.

To achieve this, however, they require serious data science talent. There is a shortage of data scientists, who can really make the difference. Bloomberg partners with academic institutions to offer grants and fellowship programmes in a bid to lure PhD researchers. They also recruit from top-tier institutions with the promise of an impactful career.

Apart from this, upskilling existing talent is another strategy that Bloomberg has adopted. They have 30 sessions online that employees can use to learn machine learning and implement it in their department. Pairing domain knowledge in niche financial markets with machine learning in existing talent is already reaping benefits. It is enabling Bloomberg stay competitive during the digital transformation.