Lemonade

Homeowners Insurance

ActuarialAssociate

$125–140k Toronto, Ontario, Canada; Canada FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Actuarial Associate at Lemonade. Skills: actuarial pricing, predictive modeling, data science. Own and develop expertise on our homeowners rating plans and rate indications. Contribute to predictive modeling projects in close partnership with the Data Science team”

What You'll Achieve.

make sure our pricing is fair, accurate, and built for scale

Industry & Context.

Homeowners Insurance
Problems you'll solve

push homeowners insurance into the future; bring actuarial discipline to some of the most complex questions in the business

Eligibility Requirements

unable to sponsor applicants for work visas, cannot consider applicants from these states: Alaska, California, Colorado, Montana, Hawaii, New Mexico and Puerto Rico

What They're Looking For.

Must Have

4+ years of P&C actuarial experience, Bachelor's degree in Actuarial Science, Mathematics, Statistics, or a related field, Working knowledge of data science programming languages like R or Python, Proficiency in SQL, Familiarity with predictive modeling concepts, including GLMs, decision trees, gradient boosting machines, random forests, and clustering, A genuine sensitivity to model transparency and ethics, Excitement about learning and growing alongside AI

Nice to Have

preferably in personal lines pricing, 5 CAS exams or 6 SOA exams

What You'll Do.

Own and develop expertise on our homeowners rating plans and rate indications

Contribute to predictive modeling projects in close partnership with the Data Science team

Craft rate filing support for new machine learning models

Identify pricing and underwriting opportunities based on ongoing monitoring of results

Build a knowledge base of competitor rating strategies and the broader competitive landscape

How You'll Work.

Team & Collaboration

Partner cross-functionally with Underwriting, Product, Claims, and BI to drive alignment and action

Full Job Description

We're looking for an Actuarial Associate to join our Pricing team and help push homeowners insurance into the future. You'll bring actuarial discipline to some of the most complex questions in the business — from distribution to underwriting, claims, and beyond — while using competitive data and data science to make sure our pricing is fair, accurate, and built for scale. We believe three things matter for every role at Lemonade: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work. Our AI-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools. IN THIS ROLE YOU’LL - Own and develop expertise on our homeowners rating plans and rate indications - Contribute to predictive modeling projects in close partnership with the Data Science team - Craft rate filing support for new machine learning models - Identify pricing and underwriting opportunities based on ongoing monitoring of results - Build a knowledge base of competitor rating strategies and the broader competitive landscape - Partner cross-functionally with Underwriting, Product, Claims, and BI to drive alignment and action - Advance your actuarial credentials through recognized qualifications and industry research, bringing those learnings directly into your work WHAT YOU’LL NEED - 4+ years of P&C actuarial experience, preferably in personal lines pricing - 5 CAS exams or 6 SOA exams, preferred - Bachelor's degree in Actuarial Science, Mathematics, Statistics, or a related field - Working knowledge of data science programming languages like R or Python, and proficiency in SQL - Familiarity with predictive modeling concepts, including GLMs, decision trees, gradient boosting machines, random forests, and clustering - A genuine sensit

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