This article was originally posted on Linkedin:
Glenn Hofmann, Chief Analytics Officer at New York Life:
Recently, I had the privilege to be a keynote speaker at two Chief Data & Analytics Officer (CDAO) conferences. Sharing how the life insurance industry is using data science provides me the opportunity to demonstrate how data science is applied in a very complex and data-rich environment. At the event in June, I also spoke about what it means to cultivate a high-performing data science and analytics team, focusing on key habits that I incorporate into my own leadership style:
- Open and consistent communication: On every project the core team includes data scientists and business stakeholders, who meet weekly to establish frequent connections at multiple levels. These regular check-ins create greater alignment and opportunities to celebrate project milestones.
- Diverse skillsets: My team consists of data scientists, data engineers, project managers, operations specialists, and change management leads, as well as employees with other skill sets. While some companies have 'data science translators' on staff to serve as a liaison between business areas and data scientists, I strongly believe in empowering (and supporting) our data scientists and data engineers to discuss their work directly with our business partners.
- Project prioritization: Projects are prioritized based on expected financial impact, business partner engagement and line of sight towards deployment. The team must stay focused on where its work will provide the most value and not undertake every potential project request that arises. Of course, we are available to consult and guide others, but we need to be careful to not stretch ourselves too thin.
- Data Science education: In offering our Data Science Academy to all employees, hosting lunch & learns, and facilitating other initiatives, the team is committed to delivering opportunities for all New York Life employees to learn about data science and its applications, regardless of their current level of understanding and experience.
And finally, making sure everyone on the team has opportunities to continue to grow their skillsets and learn from other experts is critical to the morale and strength of the team.
In fact, if you’re looking for an environment where you can build your career in data science, feel free to check out our open roles here.
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