Employee Spotlight: One Minute with Roy Wilds

Roy Wilds straddles the worlds of data science, cybersecurity, and real-world impact of Interset's technology on business value.

Roy Wilds InterseDeep data science expertise is often missing from today’s cybersecurity teams, which is why Interset has enlisted some of the best data science minds around to support our customers. As a principal data scientist at Interset, Roy Wilds straddles the worlds of data science, cybersecurity and real-world impact of our technology on business value.

Roy’s history is deeply rooted in mathematics, holding a Bachelor of Science in Mathematical Physics and a Ph.D. in Mathematics from McGill University. Roy has previously served as a mathematician for the Government of Canada, where he specialized in statistical analysis, as well as a product management lead and Chief Data Scientist for PHEMI, where he oversaw strategic direction for the company’s data science team to help drive business value for customers.

His background gives him a unique perspective on our mission and industry. Our quick Q&A with Roy shows more.

Q: How did you come into this field of work?

A: I learned early on that I liked using math to solve problems, and quickly realized that there were tons of real-world problems involving data analysis. I got lucky with an early co-op as a data miner (precursor to the more contemporary data scientist title) and never looked back.

Q: You have been in the big data and security space for a while now. How do you view where technology is taking us, and how will that impact cybersecurity practices in the future?

A: Cybersecurity is a challenging space where the growth in data due to technology advances (DLP, firewalls, etc.) has mostly outstripped the growth of algorithms and compute power to deal with that data. I believe we’re turning the corner with advanced security analytics maturing to the point where it can make sense of all that data, and help make analysts more efficient in the future.

Q: What is the most difficult aspect of math & AI for people to understand?

A: It’s often oversimplified as a “black box” that takes inputs and produces “magical” insights. At that level of abstraction, it’s terribly difficult to understand the capabilities, and importantly, the limitations.

Q: Any advice for aspiring data scientists?

A: Make sure you are passionate about working with data! Work on projects, tools, stories…anything that helps to develop your capability in working with data and telling stories about the patterns and insights present.

Rapid-fire Round

Q: Coffee or tea?

A: Coffee

Q: Classical or hip-hop?

A: Classical

Q: Tropical beach or urban cityscape?

A: Beach!

Q: Electric toothbrush or regular toothbrush?

A: Regular

Q: Ice hockey or curling?

A: A sign I’m getting older…curling.

Make sure to connect with Roy on LinkedIn.