E
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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