BENGALURU · FULLTIME
Sr Lead Specialist - Risk Analytics
Artex Risk Solutions
Bengaluru · onsite · Posted 4d ago
Sourced from
Undisclosed2–3 yrsfulltimeBengaluru
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Section · 01
About this role
Job Summary
Responsibilities
Qualifications
Key Skills and Experience
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
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.
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.
- Perform 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.
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
- 2-3 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 2 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.
- Advanced SQL coding knowledge is essential for this role.
- ArcGIS knowledge (GIS software).
- Power BI experience.
- Python or R programming knowledge is desirable but not essential.
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
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Section · 02
Skills
data qualityData Cleansingretailmathematicsadvanced sqldata enrichmentmodelerrisk analyticsgisstatisticsProgrammingAnalyticsconstructionScrubbingPythonPower BiSQLQuality Controloperationscompliance
Section · Company
About Artex Risk Solutions
Artex Risk Solutions
1997
29 years old
None
United States
₹16.1L PA avg
Avg at Artex Risk Solutions
About
Artex delivers innovative strategies for effective risk and capital management, leveraging expertise for enhanced decision-making and improved client outcomes.
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