Catastrophe Modeling Analyst

4 weeks ago


London, Greater London, United Kingdom Selby Jennings Full time

Position Overview: We are seeking a talented Catastrophe Modeling Analyst to join our team. The ideal candidate will have a strong background in quantitative analysis, data modeling, and risk assessment. You will work closely with our team of experts to develop and maintain catastrophe models, analyze data, and provide valuable insights to our clients.

Key Responsibilities:

  • Collaborate with cross-functional teams (corporate CAT, actuarial, and finance) to develop and enhance catastrophe models.
  • Provide updates on CAT events.
  • Analyze large data sets to identify trends, patterns, and correlations relevant to risk assessment and modeling.
  • Conduct sensitivity analyses and scenario testing to evaluate the potential impact of different disaster scenarios on insurance portfolios.
  • Assist in the validation and calibration of catastrophe models using historical event data and industry benchmarks.
  • Communicate findings and recommendations to internal stakeholders and clients through clear and concise reports and presentations.
  • Stay abreast of industry developments, best practices, and emerging technologies in catastrophe modeling and risk management.

Qualifications:

  • Bachelor's or Master's degree in Mathematics, Statistics, Engineering, Actuarial Science, or a related field.
  • 2-5 years of CAT experience within the insurance space.
  • Strong analytical and quantitative skills, with proficiency in statistical analysis and modeling techniques.
  • Experience with catastrophe modeling software such as RMS, AIR, or similar platforms is highly desirable.
  • Proficiency in programming languages such as Python, R, VBA, or SQL for data manipulation and analysis.
  • Excellent communication and presentation skills, with the ability to convey complex technical concepts to non-technical audiences.
  • Detail-oriented with a commitment to accuracy and quality in work output.
  • Ability to work independently as well as collaboratively in a fast-paced environment.


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