Lead Catastrophe Modelling Specialist - Risk Analytics

Introduction


Overview

Exposure Management is a high profile and continuously growing area of the Gallagher Specialty business.
The team works closely with the various Gallagher Specialty business units, which includes US Property,
International property, Facilities, Natural Resources, Marine, Construction, Financial Lines, along with the
Gallagher US Retail as well as various Gallagher retail offices globally, to assist clients in managing their
exposure data, using RMS and AIR catastrophe models to ensure clients have the correct cover in the correct
locations. The role also involves the visualisation of data and facility catastrophe modelling.

We also work closely with Lloyds and London Market Insurers to provide exposure data and modelled losses
on singular and at a portfolio level, which includes binders and facilities.

How you'll make an impact

Responsibilities:

• Play a key role in growing the Exposure Analytics and Catastrophe Modelling function within
• Gallagher Specialty which will involve taking responsibility for delivering Exposure Analytics &
• Catastrophe Modelling reports (both Moody’s RMS and AIR) for the various business units we work
  with
• Performing Schedule of Values (SOV) data cleansing and scrubbing where required
• Help create new opportunities for the business through the expansion of client and carrier analytics
• Perform quality control checks on all policies entered the Catastrophe Models across multiple lines of
  business to ensure data accuracy
• Ensure any errors found are rectified and correct before delivery of analytics to stakeholders
• Ensure standard operating procedures and minimum data quality standards are adhered to
• Ensure internal and external KPI’s and SLAs are met
• Develop new initiatives in this exciting and fast-growing area of the business

About you

Qualifications

• Educated to degree level in a subject such as Mathematics, Statistics, Geography, Engineering or
  Meteorology
• Professional qualifications such as a DIP CII or ACII or a desire to take them
• CCRA qualification is desirable but not essential

Key Skills and Experience:

• 6+ years previous Catastrophe Modelling experience gained at either an Insurer or Broker
• Experience should include Property Catastrophe Modelling ideally using RMS Risk Modeler or Risk
  Link
• Knowledge of LMP slips & the Lloyds and London Insurance Market
• Minimum of 6 years’ experience using Catastrophe Models, in particular RMS Risklink / Risk Modeler
• Good knowledge of Moody’s RMS and Verisk AIR data cleansing & coding best practices including
  when using HD models
• Good technical understanding of data enrichment, manipulation and data cleansing best practices
• Ability to work quickly and precisely with large data sets
• A keen interest in data and Natural Catastrophes
• Self-Motivated
• Good communication and presentation skills
• Proactive in finding solutions to problems without waiting to be prompted by others
• Good MS office skills particularly Microsoft Excel and ideally Power BI
• Knowledge of GIS software such as Arc GIS
• Advanced SQL coding knowledge is essential for this role
• Python or R programming knowledge is desirable but not essential

Additional Information

At Gallagher, we believe supporting our colleagues goes far beyond the role itself. For more information, visit our Benefits page.

• Competitive compensation
• Comprehensive benefits programs designed to support your well-being 
• Career development opportunities and ongoing learning 
• A collaborative, people-first culture with accessible leadership 
• The opportunity to do meaningful work with global reach and local impact 

At Gallagher, we are dedicated to building an inclusive and authentic workplace. If your past experience doesn’t align perfectly, we encourage you to join our Talent Community to stay connected to additional career opportunities. At times, we will consider transferable skills from previous roles.

Gallagher is an affirmative action/equal opportunity employer (Minorities/Females/Veterans/Disabled)