Description

Apply statistical modeling and data science techniques to solve insurance-specific problems using regression and propensity score analysis.

This role is on-site.

Responsibilities

• Apply linear, logistic, and propensity score modeling to business datasets.
• Translate statistical theory into practical applications for real-world insurance problems.
• Communicate complex analytical findings through data storytelling.
• Analyze insurance business processes and relevant KPIs.

Required Skills

• 10+ years of data science experience within the insurance domain (Health, Life, or P&C).
• Proficiency in Python, R, or SAS for data analysis and modeling.
• Deep understanding of statistical theory and practical application.
• Expertise in linear and logistic regression.
• Experience with propensity score modeling.
• Ability to work with insurance KPIs.
• Any Graduate degree.

Preferred Skills

• Experience working in regulated environments or with insurance compliance models.
• Background in customer analytics, risk modeling, or marketing analytics.

Key Skills

Education

Any Graduate