Canadian property and casualty (P&C) insurers seeking to advance their artificial intelligence (AI) capabilities will have a tough time recruiting data scientists to help them transform their data into genuine business insights, says a recruiter who works with the P&C industry.
“I think there’s a lot of talk in the marketplace that this talent is really needed, but finding that talent is extremely difficult,” says Antonella Leone, talent and acquisitions partner at DGA Careers. “The need is there and companies are moving faster than the actual academic institutes are producing these people.
“And if you are lucky enough to find one, there’s probably 10 companies courting that person, so the demand is huge and the talent is short. Anybody who does have them on staff will do everything they can to retain them.”
In the P&C sector, Intact has hired data scientists. A search on the job site Indeed.ca shows data scientists are generally sought by large corporations such as Manulife Financial, TELUS Commuications, and Google. The short supply means data scientists can command salaries as high as $300,000 to $400,000 annually.
What is a data scientist, exactly?
Unheard of as a discipline 20 or 30 years ago, many data scientists began their careers as statisticians or data analysts, SAS observes on its website. “But as big data (and big data storage and processing technologies such as Hadoop) began to grow and evolve, those roles evolved as well. Data is no longer just an afterthought for IT to handle. It’s key information that requires analysis, creative curiosity and a knack for translating high-tech ideas into new ways to turn a profit.”
Creativity is key to the role, says Patrick Vice, partner, director of products and services with Insurance-Canada.ca.
“There is a lot of data floating around out there,” he said. “The times have turned to marshal it and say, ‘I want a new perspective,’ whether it be on flood plain activity, or something like that.”
A data scientist helps correlate the data to the point where you can draw finely tuned observations from the data. They either try to create flexible ways of looking at data they already have, or they try to bring in external data sources next to their own data.
They are an integral part of AI, which relies on machine-learning to process data much more quickly than human beings do. They are behind innovations such as chatbots, online brokerages and automating an insurer’s business operations.
They are typically recruited out of universities. Leone said data scientists in Canada may most likely be recruited from the University of Waterloo or the University of Toronto.