The analytics team at Enova International Inc. has doubled since Joe DeCosmo became the financial services firm's chief analytics officer in January 2014 -- from 25 people then to 51 now. DeCosmo expects to add another five workers to the team before the end of 2015. It's an area that Chicago-based Enova is heavily investing in to try to improve its business operations and gain advantages over rivals. "We think we can compete with anybody with our analytics," he said.
The company, which offers personal and small-business loans via the Internet, has hired data scientists and analysts with a mix of computer science, statistical analysis, mathematics, economics and operations research skills. DeCosmo said they're separated into smaller teams with specific responsibilities: a predictive analytics group that builds and runs fraud detection models, a marketing analytics unit that works to identify ways to optimize marketing strategies, and a portfolio analytics outfit that handles data analysis related to underwriting and risk management.
Here are some of the steps DeCosmo has taken in managing and building an analytics team at Enova.
Setting clear analytics objectives, but not micromanaging. Data analysts get business-oriented goals -- for example, reducing fraud by a certain percentage. But then they're left to figure out the best way to get the work done. "You let the teams design the approach to meet that goal," DeCosmo said.
Recruiting hard. Enova has a recruiter dedicated to the analytics team, and the company does a lot of outreach on local college campuses, including activities such as sponsoring hackathon competitions. It also tries to take advantage of current employees' connections to add experienced analysts. "My interest in all of that," DeCosmo said, "is getting our name known among the analytics community."
Supporting a variety of analytics tools. The company has deployed software from vendors such as SAS, MicroStrategy, Pentaho and Wolfram, as well as the R and Python programming languages, and DeCosmo lets data analysts choose which ones to use. "The analysts are able to work in the technology they're most comfortable in," he said, adding that the flexibility also helps with recruiting.
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