Associate Data Scientist, Personal Lines

About the Role

The Hartford seeks an energetic and passionate Associate Data Scientist to build high-performing, empathetic analytics teams and deliver smarter product pricing decisions. This role will focus on the Auto line within the Personal Insurance Loss and Demand Modeling team. We support our product and pricing partners through the delivery of rigorously developed and tested solutions including models and implementation support. In this role, you will contribute across the full model lifecycle, from data exploration and feature engineering through model development, validation, implementation, and monitoring. Generalized linear models are a core component of our work, alongside machine learning techniques such as gradient boosted trees and modern MLOps practices. You will also help improve the tools, workflows, and standards that enable the team to support a broad and evolving portfolio of models.

This role will have a Hybrid work schedule, with the expectation of working in an office (Columbus, OH, Chicago, IL, Hartford, CT or Charlotte, NC) 3 days a week (Tuesday through Thursday).

Key Responsibilities

• Develop and evaluate predictive modeling solutions that support the personal auto class plan, unconstrained loss models, and customer demand models.
• Evaluate 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.
• Collaborate with team members to plan and deliver projects in an Agile framework.
• Develop and maintain analytic packages to facilitate data collection, analysis, and modeling.
• Stay up to date on machine learning techniques, apply them to your work, and share them with your team and the broader data science community.
• Ensure analytic reproducibility, conduct code reviews, and maintain git hygiene.
• Share results with partners and make recommendations based on outcomes.

Requirements & Tech Stack

• 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.

Qualifications

• Master's degree in Statistics, Data Science, Mathematics, Actuarial Science, Economics, Engineering, Physics, or similar analytical field.
• Experience in the insurance industry, particularly in loss cost modeling or personal insurance products, is a plus but not required. Candidates with relevant modeling experience from other industries are encouraged to apply.
• Able to independently research solutions, identify when you are stuck, and seek help when needed.
• Strong interest in identifying and eliminating workflow inefficiencies. Passionate about tracking down the root cause of problems and identifying tradeoffs.
• Demonstrated experience with listening to collaborators and brainstorming outcomes that address their business problems.
• Committed to democratizing knowledge, evangelizing good practices, and supporting peers.

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.