Actuarial Leader - AI-Driven Pricing (Remote)

Lemonade Inc.; United States; Remote; USD 150,000 - 170,000; Full time

Benefits offered by this job

Equity

Health & wellness coverage

401(k) plan with employer match

20 paid vacation days

Parental leave

Job summary

Lemonade is seeking a driven Actuarial Leader to guide a talented team and build the most innovative car insurance on the market. This role accelerates growth across states and expands analytical capabilities with cutting-edge machine learning.

You will own pricing models, manage rate levels, and apply actuarial discipline across distribution, underwriting, claims, and finance. Collaboration with data science and external data will optimize fairness and pricing accuracy.

Qualifications

• 6+ years of P&C actuarial experience, FCAS preferred
• 2+ years leading and managing a team
• B.S. in Actuarial Science, Mathematics, Statistics, or related field
• Proficiency in Python and SQL
• Predictive modeling knowledge (GLMs, trees, boosting, RFs, clustering)
• Personal lines pricing experience a plus
• Ability to work in a remote environment
• Record of interest in AI/ML tools

Responsibilities

• Own state rating plans and rate indications for Lemonade
• Lead development and integration of predictive modeling projects with Data Science team
• Develop rates and pricing strategy for product expansions into new states
• Assist in design and development of internal pricing tools for analytics
• Craft rate filing support for new ML models
• Recommend pricing and underwriting actions based on monitoring results
• Build knowledge base of competitor rating strategies
• Manage and develop a team of actuaries at various levels

Job description

Lemonade is seeking a driven Actuarial Leader to guide a talented team and build the most innovative car insurance on the market. This role accelerates growth across states and expands analytical capabilities with cutting-edge machine learning.

You will own pricing models, manage rate levels, and apply actuarial discipline across distribution, underwriting, claims, and finance. Collaboration with data science and external data will optimize fairness and pricing accuracy.