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.
Requirements
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
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
Candidate must be authorized to work in the US without company sponsorship.
Benefits
Other rewards may include short-term or annual bonuses
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