We are looking for a passionate Risk Analyst to help our product and modelling teams develop asset level and exposure data assets, as well as improve name matching abilities.
Project Overview: Our modular climate risk offering is built to meet the complex needs of insurers, banks, corporates, and real estate investors.
Through the SaaS platform, Location Risk Intelligence, we deliver an advanced geospatial solution that empowers clients with location-specific climate risk data worldwide, enabling them to make informed decisions on climate risk reporting, underwriting, and investments.
Requirements: Advanced knowledge of catastrophe modelling, climate risk analytics, or quantitative model validation Proven experience with statistical testing, performance metrics, and uncertainty analysis Proficiency in programming and data analysis (Python, R, MATLAB) and handling large datasets Experience with geospatial data handling (e.G., GIS tools, netCDF, shapefiles) Experience benchmarking or validating risk models in insurance, reinsurance, or research settings Familiarity with construction typologies, and post-event damage datasets desirable Strong problem-solving skills and ability to work in interdisciplinary teams Strong communication skills, clear documentation habits, and readiness to collaborate remotely with multidisciplinary teams Responsibilities: Design and execute structured performance tests for physical climate risk models across multiple hazards Develop validation datasets with event data, loss data, and peer-model outputs Benchmark model results against historical events, third-party models, and industry standards Identify strengths, weaknesses, and limitations of current models and propose actionable enhancements Develop and refine vulnerability assumptions for multiple hazards, incorporating empirical loss data,engineering insights, and expert judgement Analyse the influence of building design, materials, and defensible-space features on property damage potential Document methodologies, results, validation processes, and produce reproducible workflows