Senior Data Scientist - Personal Insurance Auto

ng sophistication. Your analyses and models will directly support decisions pertaining to data strategy, risk management, and pricing. Generalized linear models are a core technical component of this role, but you will also work with other machine learning techniques (e.g. gradient boosted trees) and MLOps frameworks. In addition, due to the number and complexity of the models we support, this role will entail significant workflow organization and management to ensure we can execute and iterate efficiently.

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).

Responsibilities:

• End-to-end project leadership of predictive modeling solutions that support the auto class plan.

• Mentoring and guidance of junior talent without direct reporting relationships

• 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.

• 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 broader data science community.

• Ensure analytic reproducibility, conduct code reviews, and maintain git hygiene. Promote good practices in literate, navigable, and unit-tested code.

• Share results to the business and make recommendations based on outcomes.

Experiences & Skills

• Master's degree in Statistics, Data Science, Mathematics, Actuarial Science, Economics, Engineering, Physics, or similar analytical field.

• At least 5 years in the insurance industry, preferably in loss cost modeling or personal insurance products. Candidates from other industries will be considered, but due to the team's proximity to the product, insurance experience will stand out.

• 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

• Able to independently research solutions, identify when you are stuck, and seek help when needed.

• Obsessed with 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.

• Experience building modeling solutions in cloud-native environments, such as SageMaker, a plus

• 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.

Compensation

The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford's total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:

$110,720 - $166,080

Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age

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