Catastrophe Modeling Analyst

Responsibilities

• Leverage third-party catastrophe models to obtain estimates of catastrophe risk of client's cedents.
• Justify and explain the CAT model outputs and ensure that they are reasonable and correct.
• Generate other reports to give a complete picture of the cedents catastrophe exposure.
• Handle with minimal supervision the cat pricing of reinsurance submissions.
• Apply machine‑learning methods (e.g., similarity scoring and clustering) to clean, align, and analyze large exposure datasets-producing reliable, decision‑ready insights.
• Produce concise analytics artifacts-written reports/summaries, interactive dashboards (e.g., Power BI, etc.), and reproducible notebooks (Python/R)

Required

Skills/Experience

• Demonstrated analytical proficiency: intermediate Excel and practical experience with Python and/or SQL (R optional) for data preparation, analysis, and automation.
• Excellent attention to detail and organizational skills.
• Strong interpersonal and communication skills.
• Ability to produce clear, concise written reports/summaries and dashboards (e.g., Power BI, etc.) for non‑technical stakeholders.
• Interest in learning about reinsurance and catastrophe modeling.
• Ready to work outside of normal office hours during peak season, including evenings, weekends, and public holidays as necessary.
• Basic knowledge of insurance and/or reinsurance terminology.
• Flexible with changing requirements and ability to learn new processes quickly.
• Experience working with large, complex datasets and applying ML techniques to support data quality, alignment, and analytic insight generation is expected.
• Familiarity with data visualization tools (e.g., Power BI, etc.) is nice‑to‑have exposure to data engineering (ETL, pipelines, warehousing) and process/report automation is beneficial.

• Graduate of any quantitative course, preferably in Mathematics, Actuarial Science, Computer Science, Information Technology, Physical Sciences, or Engineering.