COMMENT: Alternative data jobs are the best data science jobs in finance
The words “machine learning” and “artificial intelligence” seem to crop up everywhere these days. Both can sound like some sort of magic, rendering cars self driving and traders telepathic (ok, maybe not quite!). The reality is less glamorous. - A collection of various linear and non-linear techniques for regressions and classifications, doesn’t sound quite so enticing. Nor does their inescapable need for huge quantities of data.
When I say data, I don’t just mean nicely structured time series data filled with market prices, which everyone in finance uses. I mean datasets that could be news, collected from the web in text form. It could be images from satellite photography, used to estimate crop yields. It could be credit card transaction data which can be used to help predict earnings from retailers. It might be mobile phone data to help understand footfall in shops. Perhaps it might be measures of readership of news of articles, to understand what news could have most market impact.
This is alternative data, which, in a nutshell, is data which is not in common use. It is also very often sourced from outside of finance. Having a dataset which is not used as much by other market participants, could potentially provide you with an edge when it comes to understanding markets. This is particularly the case when we can combine many of these datasets together. The alternative data area is expanding rapidly, with more alternative datasets continually coming to market. Banks and funds are building up teams to deal with alternative data. It’s a growing area, which stands in sharp contrast to many other areas in finance, which are shrinking, hit by a mixture of regulation and automation.
So, what type of roles are available if you want to get involved in alternative data? There are data scientist roles. They help to make sense of all these alternative datasets, to see if they can be structured into a form that can be used to help gain insights into the markets. These roles may use machine learning as a tool to help in this task of structuring the data. They can help understand if particular datasets really have value or not.
There’s also the important role of data strategist. Data strategists act as a bridge between outside data vendors and internal clients. They scour the market for interesting new datasets. They work with data scientists and portfolio managers, to understand what types of datasets would be most relevant. Data strategists need to have technical skills related to understanding the data and for seeing potential uses for the data. They also need the business skills to negotiate and onboard data vendors. They also need to have an understanding of the types of legal questions they might need to ask data vendors. In short, it’s a very exciting and varied role.
Then there are roles in the alternative data vendors themselves, whether it’s in business development or more on the data science side. Having experience working in finance is valuable for these roles, given that these vendors are trying to sell their datasets to finance clients.
If you’re interested in learning more about alternative data in finance, check out The Book of Alternative Data, which I’m co-authoring with Alex Denev, which will be out in 2020. Alternative data really needs to be just as big a buzzword as machine learning is. I suspect it soon will be!
Saeed Amen is the founder of Cuemacro. Over the past decade and a half, Saeed Amen has developed systematic trading strategies at major investment banks including Lehman Brothers and Nomura. He is the author of Trading Thalesians: What the ancient world can teach us about trading today (Palgrave Macmillan) and is currently co-authoring The Book of Alternative Data (Wiley) with Alex Denev. Through Cuemacro, he now consults and publishes research for clients in the area of systematic trading. He has developed many Python libraries including finmarketpy and tcapy for transaction cost analysis. His clients have included major quant funds and data companies such as Bloomberg. He has presented his research at many conferences and institutions including the ECB and the Fed. He is also a co-founder of the Thalesians.
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