Data Analytics Lead – Property & Casualty
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Job Description
Data Analytics Lead at FOCUS Insurance Group driving analytical insights in P&C insurance. Collaborates with business leaders to enhance decision-making and operational efficiency.
Responsibilities:
- Design and lead end-to-end analytics projects addressing strategic business questions across P&C underwriting, claims, distribution, and client experience.
- Develop advanced statistical models, segmentation analyses, and predictive frameworks to surface trends from large, complex datasets.
- Build and own executive-level dashboards and self-service reporting tools using BI platforms (e.g., Tableau, Power BI, Looker).
- Partner with business leaders in Sales, P&C Underwriting, Operations, and Finance to translate needs into analytical project plans.
- Provide guidance, code review, and technical mentorship to junior and mid-level analysts on the team.
Requirements:
- Bachelor's degree in Statistics, Mathematics, Computer Science, Economics, Business Analytics, or a related quantitative field.
- 6+ years of progressive experience in data analytics, business intelligence, or a related role, with at least 2 years in a lead or senior capacity.
- Advanced proficiency in SQL; strong working knowledge of Python or R for analytical workflows.
- Expertise with BI and visualization tools such as Tableau, Power BI, or Looker.
- Strong understanding of data modeling, data warehousing concepts, and ETL processes.
- Knowledge of insurance products, terminology, and regulatory environment is a strong plus.
Benefits:
- Medical, Dental, Vision, Life, Pet
- Flexible Spending Account
- Competitive Salaries
- 401K Match
- Work-Life Balance: Personal Days, PTO Days, 12 Paid Holidays, Two Paid Days for Volunteer Service, Paid Parental Leave
- Short and Long-Term Disability
- Employee Support Programs, Including Mental Health
- Tuition Reimbursement
- Matching Charitable Gift Program
- Lucrative Referral Program
- Commuter Benefits
- Flexibility: Remote and Hybrid Opportunities Available
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