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AI Catastrophe Loss Estimation Developer

Carter Andersen

Full-time · Job · New_York

About the role

Catastrophe loss estimation is the actuarial foundation of reinsurance pricing, portfolio accumulation management, and regulatory capital adequacy. When a hurricane makes landfall or an earthquake strikes an urban area, the question the industry asks within hours is: what is our expected total insured loss? The models that answer that question — catastrophe models combining atmospheric science, engineering vulnerability curves, and exposure data — are among the highest-stakes ML systems in the financial industry. Our insurance analytics platform serves 8 reinsurance companies and 15 primary insurers. Catastrophe loss estimation is the most technically demanding capability we offer. Current cat models rely on vendor-supplied event sets and vulnerability functions that don't reflect recent climate shifts or the exposure composition of specific client portfolios. We're building proprietary ML-enhanced cat models that improve on vendor models for our clients' specific exposure mixes. This is a principal-level role requiring deep actuarial science and ML expertise. You'll lead cat model development: building ML-enhanced vulnerability functions from historical loss data, implementing climate-adjusted event set generation, and developing the real-time accumulation monitoring that tells underwriters when a storm system approaching landfall could breach portfolio concentration limits. The role is hybrid — 2 days per week in NYC.

Responsibilities

  • Build ML-enhanced vulnerability functions from historical loss data improving on vendor standard curves for specific exposure types
  • Implement climate-adjusted event set generation incorporating updated Atlantic basin and wildfire hazard projections
  • Develop real-time accumulation monitoring alerting underwriters when approaching storm systems threaten concentration limits
  • Create exceedance probability curve construction for portfolio return period loss estimation
  • Build secondary peril correlation models capturing demand surge, fire following earthquake, and storm surge interactions
  • Validate ML model outputs against independent vendor model benchmarks and historical event realizations

Requirements

  • 8+ years in catastrophe modeling, actuarial science, or climate risk ML with reinsurance exposure
  • Deep expertise in natural hazard science, vulnerability modeling, and insurance loss estimation
  • Strong Python and Spark skills with large-scale geospatial and exposure data processing
  • Understanding of reinsurance program structures, accumulation management, and cat model regulatory requirements
  • Track record building proprietary cat models adopted by reinsurance clients for pricing and capital decisions

Benefits

  • Hybrid: 2 days NYC, 3 days remote
  • Top-of-market total compensation for catastrophe modeling expertise
  • Comprehensive health benefits
  • $5,000 annual professional development budget
  • Significant equity participation

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Categories

data science & analytics MLOps & AI Infrastructure

Skills

python pytorch spark sql statistics

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