Job Description

Company Description

WNS (Holdings) Limited (NYSE: WNS), is a leading Business Process Management (BPM) company. We combine our deep industry knowledge with technology and analytics expertise to co-create innovative, digital-led transformational solutions with clients across 10 industries. We enable businesses in Travel, Insurance, Banking and Financial Services, Manufacturing, Retail and Consumer Packaged Goods, Shipping and Logistics, Healthcare, and Utilities to re-imagine their digital future and transform their outcomes with operational excellence.We deliver an entire spectrum of BPM services in finance and accounting, procurement, customer interaction services and human resources leveraging collaborative models that are tailored to address the unique business challenges of each client. We co-create and execute the future vision of 400+ clients with the help of our 44,000+ employees.

Technical Skills: Hands on Experience on Python, R, PowerBI/Qlik. Experience on Emblem & Radar will be an added advantage. Good understanding of General Insurance concepts - building pricing models using GLMs/ machine learning techniques and/or advanced actuarial methods, performance monitoring to track price trends and identify potential areas of adjustment. Ability to build dashboards in Power BI Contribute to development of GenAI-driven solutions for pricing automation, documentation and modeling workflows. Good to have knowledge on reserving concepts, including claims development patterns and loss ratio analyses. Strong analytical and problem solving skills along with the ability to communicate complex technical concepts clearly to non-technical stakeholders

Additional Information -

2-5 years of experience in the General Insurance. Strong communication skills - Verbal & Written. Ability to work through stringent timelines.

Qualifications

Qualification -Graduate/Master in any stream (computer science/Actuarial science/ Statistics/ Mathematics is preferred)

Qualifications

Qualification -Graduate/Master in any stream (computer science/Actuarial science is preferred)