Catastrophe Risk Modeler

National Association of Insurance Commissioners (NAIC)•Kansas City, MO

3d•$125,000 - $162,000•Remote

About The Position

The Regulatory Affairs division of the National Association of Insurance Commissioners (NAIC) has an exciting opportunity for a Catastrophe Risk Modeler. The Catastrophe Risk Modeler performs catastrophe modeling, data analytics, and geospatial analysis to support state insurance regulators in understanding and managing natural catastrophe risk. This position uses commercial catastrophe models, insurance and notional exposure datasets, and other analytical tools to help states prioritize resilience initiatives, mitigation programs, grant funding, and other investments based on hazard, exposure, vulnerability, mitigation potential, and expected loss reduction. This is a full-time remote position. Residency within the United States is required.

Requirements

Bachelor’s or master’s degree in a quantitative or technical field such as applied mathematics, actuarial science, statistics, data science, insurance, risk management, engineering, physics, atmospheric science, geography, geospatial science, or a related discipline is required and 5+ years of experience in delivering complex catastrophe modeling, data analysis, geospatial analysis, or applied risk analytics, including significant experience within property insurance, reinsurance, catastrophe risk, or a closely related field or an equivalent combination of education and experience.
Hands-on experience with at least one recognized commercial catastrophe modeling platform, such as Moody’s RMS, Verisk, or another comparable vendor platform, is required. Experience should include preparing model data, configuring and executing analyses, evaluating results, and communicating model assumptions and limitations.
Proficiency with Microsoft Excel, SQL, GIS tools (e.g., ArcGIS), cloud analytics, and at least one programming or statistical language, such as Python, R, or SAS, is required.
Strong written and verbal communication skills are required, including the ability to explain complex catastrophe modeling concepts and analytical conclusions to regulators, executives, technical experts, and nontechnical audiences.
Residency within the United States is required.
Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.

Nice To Haves

Knowledge of emerging risks, including physical climate risk, casualty catastrophe risk, or cyber catastrophe risk, is beneficial.
Relevant professional credentials—such as CPCU, CEEM, CCRA, CSCR, CCRMP, ARM, ARe, or actuarial designation.

Responsibilities

Works with the Sr. Catastrophe Risk Modeler and Sr. Manager of CAT Modeling Center of Excellence to execute assigned catastrophe modeling projects, including identifying appropriate data and models, selecting methodologies, establishing assumptions, designing quality-control procedures such as documentation and validation/peer-review, managing technical timelines, and communicating results and limitations.
Executes complex catastrophe model analyses involving multiple vendor platforms, perils, geographic regions, portfolios, or regulatory applications. Evaluates differences among models and scenarios through sensitivity testing, uncertainty analysis, benchmarking, and reconciliation of results.
With guidance from the Sr. Catastrophe Risk Modeler, prepares and validates exposure data, configures and executes catastrophe model runs, conducts quality assurance and reasonableness reviews, analyzes model outputs, and articulates model limitations.
Works on the development of analytical tools, interactive dashboards, maps, tables, charts, and other visual products using platforms such as Tableau, Power BI, and ArcGIS.
Assists in delivering regulator education, including introductory programs such as CAT 101 and advanced training on model components, assumptions, uncertainty, validation, and regulatory applications.
Assists in evaluating new catastrophe model versions, data sources, analytical tools, artificial intelligence applications, and emerging methodologies.