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Lemonade

Sr Decision Science Analyst, Capital & CAT Modeling

Posted Yesterday
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Remote
Hiring Remotely in USA
130K-145K Annually
Senior level
Remote
Hiring Remotely in USA
130K-145K Annually
Senior level
Design and own capital and catastrophe modeling frameworks to evaluate policy- and portfolio-level risk and capital. Run and integrate catastrophe model outputs, build analytical tools and data structures, assess reinsurance impacts, and translate results into actionable recommendations for underwriting, finance, and portfolio strategy.
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We are looking for a Senior Decision Scientist to design and own mathematical risk thresholds across our insurance products.
In this role, you will operate at the intersection of catastrophe modeling, capital modeling, and portfolio strategy, ensuring that each policy we write is evaluated against explicit capital requirements and aligned with long term growth and risk objectives. You’ll dive into the details, build practical solutions, and turn well grounded analysis into clear, actionable decisions that move the business forward.

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 build the capital modeling framework, running catastrophe models and integrating their outputs to support portfolio management, growth planning, and strategic decision making

  • Build and maintain the analytical tools and data structures used for catastrophe, capital, and portfolio modeling

  • Evaluate how individual policies and portfolio changes impact capital requirements, PMLs, tail risk, and expected returns

  • Partner closely with insurance, finance, and underwriting teams to assess the impact of reinsurance structures, retentions, and limits on portfolio risk and capital efficiency

  • Translate capital and catastrophe model results into clear takeaways and recommendations that help guide growth and risk decisions

  • Approach problems with curiosity and good judgment, challenging assumptions when needed and contributing to a team that values learning and shared ownership

What you’ll need
  • 4+ years of experience in CAT modeling, capital modeling, or quantitative risk analytics within P&C insurance or reinsurance

  • Solid grounding in statistics and probability, especially as applied to extreme events and heavy-tailed distributions

  • Proficiency in data science programming languages such as R or Python, and database query tools such as SQL

  • Bachelor’s degree in Data Science, Economics, Mathematics, Statistics, Actuarial Science, or a related field

  • Understanding reinsurance structures and how they impact portfolio risk and capital is a plus

  • Ability to work remotely

  • Enthusiasm about learning and adapting to the exciting world of AI – a commitment to exploring this field is a fundamental part of our culture.

*Please note that we are unable to sponsor applicants for work visas

Unfortunately, we cannot consider applicants from these states: Alaska, California, Colorado, Montana, Hawaii, New Mexico and Puerto Rico.

Lemonade's US base salary range for this full-time position is $125,000 - $140,000 plus equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Our competitive total rewards package includes comprehensive health & wellness coverage, equity, a 401(k) plan with employer match, 20 paid vacation days, and parental leave. Speak to your recruiter to hear more about the specific salary range for your preferred location and our total rewards package.

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