VP, Insurance Data Science
Openly, Remote - United States
What you need
- 15+ years of experience in data science or a closely related quantitative/actuarial field, with at least 8 years in a leadership role managing highly analytical teams.
- Deep technical fluency in predictive modeling — GLMs, gradient boosting, and modern ML methods — with demonstrated experience delivering production models in a regulated, data-intensive industry.
- Proven experience across both cost modeling (loss ratio, frequency, severity, pure premium) and demand modeling (conversion, retention, price elasticity), and the strategic judgment to prioritize across a portfolio of modeling initiatives.
- Extensive experience in personal lines insurance with models directly driving pricing and underwriting decisions; familiarity with the actuarial context in which data science operates is a strong differentiator.
- Track record of building and leading high-performing data science teams — not just managing delivery, but developing people, raising the technical bar, and creating an environment where rigorous analytical work thrives.