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Building a Holistic Data Science Function at New York Life Insurance

In this episode, we talk about how Glenn Hofmann built New York Life Insurance’s 50-person data science and AI function, how they utilize skillsets to offer different career paths for data scientists, and much more.
Jun 2022

Photo of Glenn Hofmann
Glenn Hofmann

Glenn Hofmann is the Chief Analytics Officer at New York Life Insurance Company (NYL), a Fortune 100 firm, where he applies extensive knowledge in applied statistics, analytics, and data infrastructure with deep expertise in building and leading large teams of data- and analytics-focused professionals from the ground up to influence strategy as well as transactional decisions through data science-driven innovation. Glenn brings with him over 20 years of global leadership experience in data, analytics, and artificial intelligence across the US, Germany, and South Africa.

Photo of Adel Nehme
Adel Nehme

Adel is a Data Science educator, speaker, and Evangelist at DataCamp where he has released various courses and live training on data analysis, machine learning, and data engineering. He is passionate about spreading data skills and data literacy throughout organizations and the intersection of technology and society. He has an MSc in Data Science and Business Analytics. In his free time, you can find him hanging out with his cat Louis.

Key Takeaways


The key to creating value with data science is to always envision how a project or product will be implemented and utilize a smooth system of fast deployment.


When developing data science functions, data leaders from the onset should enable career pathways for their data talent.


Model governance leaders need to not only understand all of the algorithms and data, but they need to understand regulation and legislation for both the present and the future.

Key Quotes

Data Scientists have to constantly talk with stakeholders, consistently helping them understand the value of data science as it relates to their business problems, and data scientists need to understand those business problems and everyday concerns. Great stakeholder relationships can really only happen when both parties understand where they are each coming from.

We want to build great models that we can trust and that are diligent and sound, but if they aren’t deployable, then they are pointless. As soon as the first model reaches a stage where you consider how it will be deployed, the ML ops function can help data scientists build models that are ready for production throughout the process, both from a code perspective and from a data perspective.


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: Grea... See more

t 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.



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