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Catastrophe Modeling Analyst

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Onsite

Catastrophe Modeling Analyst

ECLARO

Catastrophe Modeling Analyst

Skills

SQL QueriesData EngineeringPythonData VisualizationRData AnalysisMachine LearningPower BI

1 month ago

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Important information


Contract type:

Other

Location:

Taguig, Metro Manila, Philippines

Work mode:

Onsite

Published on:

15 September 2026

What they need


Role: 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.

Education

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

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