Contribute and lead key phases of end-to-end model development including problem definition, data exploration, model development, validation, and monitoring.
Engage in the development and enhancement of predictive models — while thoughtfully assessing alternative approaches and techniques to support loss, territory/Geography, and emerging claim level modeling needs.
Partner with Data Engineering and MLOps teams to support deployment and monitoring.
Partner with Actuarial, Underwriting, Product, Data Engineering, and Governance teams to align modeling work with business strategies.
Communicate findings clearly to technical and non-technical audiences.
Explore new modeling methods, data sources, and tools.
Provide peer learning, code reviews, and technical guidance.
Support continuous improvement of modeling standards.

Requirements

5+ years experience developing statistical or ML models.
Master’s/PhD in Statistics, Data Science, Applied Math, Computer Science, Actuarial Science, or related field.
Strong Python and SQL skills; experience with Git and cloud ML environments.
Ability to collaborate across functions and communicate insights effectively.

Benefits

Engage in high-visibility enterprise modeling initiatives.
Work with modern analytical tools, ML, and generative AI.
Work at the intersection of Data Science and Actuarial Science, partnering closely with diverse technical and business teams.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

statistical modelsmachine learning modelsdata explorationmodel validationmodel monitoringpredictive modelingdata analysisPythonSQLGit

Soft Skills

collaborationcommunicationpeer learningtechnical guidanceproblem definition

Certifications

Master’s degreePhD