Director of Insurance Analytics
PA - RadnorAll locationsPA - RadnorNJ-WarrenGA-AtlantaOH-CincinnatiIL-ChicagoNY-NYC (Ave of Americas)TX-Dallas (Addison) Hybrid
$225,000–$250,000 a yearJobFig found this opening at its original source and checks that it remains available.
About the role
We are seeking a hands-on analytics Manager/Director to lead a portfolio of high-impact initiatives, drive cross-functional delivery, and personally develop analytical solutions. This role is ideal for a builder-leader who thrives on both technical execution and stakeholder engagement. You will lead the full analytics lifecycle end-to-end: problem framing and hypothesis definition, experimentation and modeling, and operationalization into repeatable decision support with monitoring and sustained adoption. Success is measured by measurable business outcomes, not slideware. This position is a hands-on program/initiative lead and may include people leadership. Scope and people-management responsibilities will vary by final level (Manager vs Director) and organizational needs.
What you'll bring
- To perform this job successfully, an individual must be able to perform each essential duty.
- The requirements listed below are representative of the knowledge, skill, and/or ability required.
- Bachelor’s degree in Statistics, Computer Science, Mathematics, Economics, or a related quantitative field or equivalent practical experience.
- 7+ years of experience in analytics or data science, with a track record of delivering measurable business impact.
- 2+ years leading analytics initiatives and/or mentoring technical team members in a senior/lead capacity.
- Proficiency in Python and SQL
- ability to write production-quality analytical code and review others’ work.
- Strong communication skills with ability to influence senior stakeholders
- translate technical work into clear decisions, actions, and tradeoffs
- Experience productionizing analytics/model outputs in partnership with engineering teams (e.g., APIs/services, CI/CD, containerization).
- Experience with cloud-based analytics environments and modern ML frameworks (e.g., scikit-learn, XGBoost).
- Experience with experimentation, causal inference, or rigorous measurement approaches in business settings.