Jesse Shaw is one of two principle data scientists at LexisNexis. He's using machine learning to recognize fraud on behalf of client institutions, typically financial & insurance. I sat down with Jesse on the Cold Star Project to get a reality check on what AI really is, its capabilities, and how you can get into the data science field. The truth is, we're all going to become data scientists to some degree.  

We first clear up the misconceptions of what AI is. Marketers are fond of saying they "use AI" when all they're really employing are nested If-Then functions. This is especially irritating to me, so Jesse's definition will tell you what Artificial Intelligence really is.  

While a huge data lake has been collected over the past decade, and is continually being added to, this data is inert by itself. Jesse shares some ways machine learning is being used to sort this data into useful results.  

In fact, he is using AI to reveal insights about fraud in his role at LexisNexis. Without giving away any trade secrets we discuss how AI is helping Jesse see fraud through graphical data representation.  

In follow-up, I ask Jesse what tools or abilities he would appreciate now--that he wouldn't have thought of a year or two ago--that would make his work easier. On the way to these LexisNexis has developed a  new programming language called KEL: it makes running queries with AI easy even for non-techie presidents and CEOs.  

Finally, I ask Jesse if he believes more roles like his will be springing up, or if data science remain a relatively small niche. If you're interested in getting into the data science field or becoming a data scientist, you'll want to hear this--because Jesse didn't come into the role by a regular path. 

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