Closer to a data science crossover than a modelling-heavy IC role because production engineering and organization-wide modernization matter as much as model construction.
Associate P&C actuarial data science role in New York, supporting Commercial Lines pricing and risk segmentation. You’ll develop GLM and machine learning models, work with large insurance datasets, build production-ready analytics, and advise actuarial teams and senior leadership. The role is hybrid, with three days in the NYC office and two remote.
Requirements
Must have
Bachelor's degree in Actuarial Science, Statistics, Mathematics, Data Science, Computer Science, Engineering, Economics, or related quantitative field
3-5 years of experience in Actuarial Data Science field
Experience developing predictive models and advanced analytical solutions in a business environment
Strong proficiency in Python, including development of production-quality analytical code
Advanced SQL skills for large-scale data extraction, transformation, and analysis
Experience working with large, complex datasets and statistical modeling techniques
Strong communication, collaboration, problem-solving, and stakeholder management skills
Ability to work independently in a fast-paced environment
Must be authorized to work in the U.S. without current or future sponsorship
Strongly preferred
Experience with actuarial pricing methodologies
Experience with Commercial Lines products such as Workers Compensation, Commercial Auto, General Liability, BOP, Professional Liability, Umbrella, or similar
Experience leading actuarial or analytical modernization initiatives
Nice to have
Familiarity with Git or Azure DevOps, code review processes, CI/CD concepts, package management, collaborative development workflows
Familiarity with model governance, validation, and monitoring frameworks
Skills & Domain
ToolsGit_GitHubExcel
DomainCommercial lines pricing methodologiesPredictive modeling and advanced analytical solutions
