Top Insurance Data Science Jobs in New York City, NY

Principal Data Scientist role focuses on developing predictive models for underwriting in healthcare using advanced machine learning techniques and large datasets. Collaborate across teams to drive data-driven solutions and stay updated on industry trends.

As a Data Scientist, you will leverage data to improve client experiences by designing metrics, running experiments, and creating data models. You'll support cross-functional teams in decision-making while crafting the company's data science culture.

Lead Data Scientist responsible for managing projects in data engineering, model design, and infrastructure deployment while collaborating with clients to develop data-driven solutions.

The Lead Data Scientist will manage projects to meet business goals, develop data pipelines, and collaborate with teams to support strategic decisions.

As a Senior Data Scientist, you'll lead AI/ML projects, contribute to end-to-end model lifecycle, and collaborate with cross-functional teams to drive innovation in insurance underwriting.

Design and implement data pipelines, perform root cause analysis, ensure data accuracy, conduct exploratory analysis, develop predictive models, and create visualizations to optimize risk segmentation and support decision-making.

The Actuarial Analyst role involves data analytics, performance monitoring, competitive intelligence, and market analysis to optimize insurance products.

The Data Scientist will develop and maintain models for ratemaking and underwriting, educate stakeholders, and contribute to decision-making processes in the insurance sector.

As a Data Scientist Tech Lead, you will guide a team in developing and deploying machine learning and generative AI systems, mentor other data scientists, and collaborate with various stakeholders to drive analytics solutions.

The Senior Manager oversees the Data Science team, designing algorithms to improve claim payment accuracy and mitigate fraud within CareSource health plans. Responsibilities include managing data science functions, mentoring team members, and collaborating on innovative analytic solutions to enhance operational efficiency.