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Job Description

The Catastrophe Modeling Analyst quantifies catastrophe risk on Arch Re’s inward reinsurance contracts, analyzing client exposure and model outputs in third‑party platforms, researching catastrophe risk, and producing clear exposure reports. In parallel, the role leverages scalable data‑science and machine‑learning (e.g., similarity scoring, clustering) to clean, align, and analyze large exposure datasets, producing decision‑ready insights while fulfilling core catastrophe modeling duties

Responsibilities

• Leverage third-party catastrophe models to obtain estimates of catastrophe risk of Arch Re'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.

• Education

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

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