Chubb is a world leader in insurance. With operations in 54 countries, Chubb provides commercial and personal property and casualty insurance, personal accident and supplemental health insurance, reinsurance and life insurance to a diverse group of clients. The company is distinguished by its extensive product and service offerings, broad distribution capabilities, exceptional financial strength, underwriting excellence, superior claims handling expertise and local operations globally.

KEY OBJECTIVE:

The Global Analytics team is seeking a Sr. Data Scientist to join our fast-paced, dynamic team responsible for developing and delivering machine learning models and AI solutions in Claims space. Our work directly impacts our business partners and positively influence business metrics.
This position offers the opportunity to leverage the entire spectrum of AI tools including transformer-based models, LLMs and RAG-based approaches towards solving a wide variety of business problems at Chubb. The role further requires one to be proficient in managing and leading projects from conception to completion.

• Utilize an extensive understanding of AI and machine learning to develop predictive models and algorithmic solutions.Build analytical solutions for complex P&C claims operations.
• Collaborate with claims business partners and peers in the organization to understand and scope the problem, gather business requirements, and plan projects tasks and timelines.
• Build data pipelines to create insurance specific risk signals from external data sources.
• Develop AI Ops processes for efficient development and deployment of models.
• Research, recommend, and implement AI methodologies appropriate for the given risk assessment problem or analysis.
• Mentor junior data scientists towards improving their domain knowledge, modeling capabilities, and developing problem solving skills.
• Lead end-to-end projects using advanced data modeling and analysis techniques to discover insights that guide strategic decisions and uncover opportunities.