Top Insurance Data & Analytics Jobs in Chicago, IL

The UAT Lead is responsible for validating developed solutions in the Agile framework, collaborating with product teams to create test strategies, executing test plans, and facilitating alignment across project teams.

The Lead Data Analyst (Network) drives data-driven initiatives, dissects datasets, and provides actionable insights to enhance business performance and network efficiency.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

The role involves analyzing insurance classification frameworks, creating mapping documentation, and communicating classification rationale while working with stakeholders.

Analyze product and customer metrics to improve platform performance, develop reporting frameworks, and collaborate on data-driven strategies with cross-functional teams.

The Product Analyst will analyze product performance, develop reports, and collaborate with stakeholders to enhance pet insurance offerings through data-driven insights.

As an Actuarial Data Scientist, you will develop and deploy commercial auto pricing models, collaborate with actuaries and underwriters, design feature pipelines, and communicate findings effectively. This role requires strong statistical skills and experience with predictive modeling in insurance.

The Sr Actuarial Analyst will lead actuarial projects, mentor team members, model risk, evaluate pricing, and enhance analytics within the team.

Lead the pricing function for Management Liability, perform pricing studies, maintain advanced models, and collaborate with underwriting and claims departments.

The Associate Actuary will engage in various assignments involving M&A consulting, financial modeling, valuation, and actuarial due diligence, aiming to enhance client service and strategic decision-making.

Lead design and delivery of enterprise data platforms using Snowflake and AWS. Architect ELT/CDC pipelines, implement Data Vault 2.0 and dimensional models, optimize Snowflake performance, mentor engineers, partner with stakeholders, and ensure operational reliability and observability for analytics and BI workloads.