Associate, Reinsurance Pricing Job in Gurgaon at AXA XL

AXA XL is hiring for the role of Associate, Reinsurance Pricing!

Responsibilities of the Candidate:

• Gather and interpret information from clients, brokers, and underwriters.
• Work with our customers to understand exposures, review modeling results, explain modeling limitations and resolve issues promptly.
• Model catastrophe reinsurance structures and strategies using primarily RMS (Risk Browser & Risk Link) and other vendors to support actuarial/underwriting functions primarily serving Facultative Reinsurance lines.
• Assess the data quality and make necessary assumptions for modeling.  Create reports for client exposure and modeled losses.
• Support our other reinsurance groups, analyzing both individual risk and portfolio level risk where required.
• Collate data for various business units to prepare for Fac portfolio aggregation, apply or update policy terms based on endorsement or slips.
• Preparing the monthly\quarterly aggregation reports, building out in-force portfolios and providing the corresponding loss distributions by peril/region.
• Support projects that improve our function such as model validation, tool development, and data mining.
• Develop processes & scripts to facilitate data transfers and optimize risk calculations.
• Perform other job-related duties as assigned.

Requirements:

• Relevant years of experience in a cat modeling role. Experience with RMS products, especially RiskLink & Risk browser is necessary.  AIR products experience preferred, but not required.
• Undergraduate-level knowledge of statistical and mathematical concepts required.
• Excellent computer skills for using Microsoft Office (Excel, Access), SQL, and VBA.
• Should be deadline driven and have ability to work under pressure.
• Good analytical, interpersonal, communication and organizational skills. 
• Understanding of reinsurance terms and conditions preferred but not required.
• Ability to work with large amount of data, complex instructions, multiple exceptions, and layered categorizations.