Building a Holistic Data Science Function at New York Life Insurance

Adel Nehme: Hello everyone. This is Adel data science, evangelist, and educator at data camp. When we talk about data maturity on the podcast and the process of becoming data-driven, we often lose sight of the sheer amount of effort and work it takes to build a strong data science function within an organization, building data teams, creating connection with the rest of the organization, evangelizing the data teams, work, training others on data literacy in building a data.

Culture is no easy. But today's guest approaches, building high impact data science functions with the clarity few have. Glenn Hoffman is the chief analytics officer at New York life insurance. He's an experienced senior executive in insurance and financial services who currently leads the corporate 50-person data science and AI function at New York life.

He is responsible for the foundation's relationships with many internal groups and a great variety of projects, and also leads their data science academy, their internal education program for all New York life. Throughout the episode, we talk about how he built the team on New York life insurance, the different skills they optimize for delivering career pathways, for data scientists, building ML ops, and model governance teams.

How to build relationships and work at the work of the data team, the ins and outs of the internal data science academy. And much more. If you enjoyed this episode, make sure to rate, subscribe, and comment, but only if you liked it now. Glenn. It's great to have you on the show.

Glenn Hoffman: Great to be here. Thanks for inviting me.

Adel Nehme: I'm very excited to speak with you about your work leading data and New York life insurance. How to organize effective data teams, delivering impactful data science, use cases within life insurance and much more, but before, can you give us a bit of a background about yourself and what got you to where you are?

Glenn Hoffman: Well, let me start with the current position and how it got to that. And then I can go a little further back if needed. Yeah. So I've been at New York life a little over five years, actually, even before coming here, I was watching the life insurance industry for bed and were excited about it.

And then, it seems like the white moment, about five years ago to join the industry and do some data science because I mean, life insurance is. It's a fairly traditional industry, but now over the last few years it's been a light for change. And the nice thing is it has a great variety of interesting problems that we'll talk about more.

Then we can build the first solution, right? It's a New York life is a big place, you know, it's most of the fortune 100 financial companies. So there's lots of challenges. And most of them we tackle for the first. So I that's where the exciting to me, to be at a place where you can be truly innovative and build for solutions for really interesting problems.
So that's why I joined the company. And since then I've filmed the function and the team little photo back. I mean, I've been in data science and what we used to call statistics. I started out as a stats professor in a long, long time ago, did five years of that and then joined industry. And basically it did every job.

It wasn't a hands-on predictive model or early in my career, or, you know, eventually got more and more responsibility. And then over the last decade or so I've been building data science functions for a few different kinds.