AI Reinsurance Exposure Analytics Developer

Quilvex Systems

Remote · $130k – $160k / year · Full-time · Posted 4 months ago

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About the role

Our clients include Lloyd's of London syndicates, Bermuda reinsurers, and global ILS funds who use Quilvex to underwrite insurance risk more precisely. Reinsurance exposure analytics — understanding the aggregate risk concentration in a portfolio of ceded risks — is a technically demanding problem that sits at the intersection of actuarial science, geospatial risk modeling, and ML. In this senior role, you'll build the models that aggregate cedant exposure data, model correlation between risks under catastrophe scenarios, and score portfolio concentration risk across geographic, peril, and counterparty dimensions. You'll work with our actuarial team to validate model logic against treaty terms and rating agency capital frameworks. This is a domain-heavy role, and we expect significant learning investment up front. We have actuaries who will teach you reinsurance structure — we need you to bring the ML engineering depth. Explainability is non-negotiable: every output that feeds a capital allocation decision must have a clear, auditable reasoning chain.

Fully remote.

Responsibilities

Build models for aggregate reinsurance exposure concentration and portfolio risk scoring
Develop correlation models for catastrophe scenario stress testing
Collaborate with actuarial team to validate outputs against treaty terms and capital frameworks
Own explainability layer for all models used in capital allocation decisions
Maintain historical catastrophe event datasets and model backtesting pipelines

Requirements

5+ years data science experience in financial services or insurance
Strong Python and SQL skills
Experience with statistical risk modeling and portfolio analytics
Familiarity with reinsurance, catastrophe modeling, or actuarial concepts is a strong plus
Deep commitment to model explainability and audit trail quality