The Hartford seeks to enhance product pricing sophistication within the Auto Line of the Personal Insurance Loss and Demand Modeling Team by developing high-performing analytics and models.
Requirements
- Comfort with generalized linear models and related techniques (e.g. ridge and lasso regression, GAMs) and the math behind them.
- Hands-on experience with Python and a track record of delivering clean, literate code in a reproducible environment.
- Experience using GitHub for version control, documentation, code collaboration, and technical project management
- Experience building modeling solutions in cloud-native environments, such as SageMaker, a plus
Responsibilities
- End-to-end project leadership of predictive modeling solutions that support the auto class plan.
- Test new data sources and modeling approaches to boost product performance. Engineer features to uncover additional signal for our models.
- Support model implementations with state Departments of Insurance (DOI), serving as a subject matter expert on the models you build and responding to DOI queries.
- Maintain a portfolio of repositories and scripts and suggest improvements to workflows. Assist in the modernization of model deployments using our cloud-based MLOps tools.
- Develop and maintain analytic packages to facilitate data collection, analysis, and modeling.
- Ensure analytic reproducibility, conduct code reviews, and maintain git hygiene. Promote good practices in literate, navigable, and unit-tested code.
- Stay up to date on machine learning techniques, apply them to your work, and share them with your team and broader data science community.
Other
- Mentoring and guidance of junior talent without direct reporting relationships
- Collaborate with team members to plan and deliver projects in an Agile framework.
- Share results to the business and make recommendations based on outcomes.
- At least 5 years in the insurance industry, preferably in loss cost modeling or personal insurance products.
- Candidate must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.
