Data Scientist III

About the role

Hagerty is a company built by drivers for drivers. We put our members at the center of everything we do and are dedicated to making it easier and more enjoyable for enthusiasts to drive and celebrate the machines they love. We're proud to be the world’s largest insurer of collectible and enthusiast vehicles and are home to the Hagerty Drivers Club, the world’s largest car club. Our Marketplace business presents live and digital sales across the U.S. and Europe, we host a number of driving events and concours, and our award-winning automotive journalists produce the most popular car magazine globally, alongside internationally awarded videos. We're committed to Never Stop Driving. Ready to get in the driver’s seat? Join us!

Responsibilities

• Build the customer identity and personalization layer that powers how we understand and engage members across our subscription and property & casualty (P&C) insurance products.
• Work in close partnership with ML Ops, Data Engineering, and Marketing/Product to create a unified, resolved view of each member across our data ecosystem.
• Create matching systems that pair a strong deterministic foundation with probabilistic matching at scale, balancing precision, recall, and cost.
• Partner with Data Engineering and the Customer Data Platform (CDP) team to land resolved identities and audiences into production pipelines and activation systems.
• Evolve the identity layer toward graph-based representations of members, vehicles, and policy/membership relationships.
• Design, build, and evaluate recommendation and personalization models, including content-based and hybrid approaches, to surface next-best-product and content across our insurance and subscription offerings.
• Develop cold-start strategies that deliver relevant experiences to new and low-engagement members.
• Develop next-best-action and journey-signal models that translate behavior into triggers the business can act on, supporting cross-sell and upsell across insurance and membership products.
• Own full modeling workflows: exploratory analysis, feature engineering, model development, cross-validation, and performance monitoring.
• Ship models as reliable production services in partnership with ML Ops, contributing to containerized deployments, automated sing, and monitoring.
• Source and analyze features from Snowflake, SQL Server, and AWS RDS Postgres, and work with Data Engineering to promote proven features into scalable pipelines.
• Contribute to the team's modeling standards through maintainable, well-documented, sable code.
• Communicate methods, results, and trade-offs clearly to technical and non-technical partners.

Requirements

• Hands-on experience designing, training, and deploying ML models in production.
• Proficient in Python and modern ML frameworks such as scikit-learn and XGBoost.
• Strong in SQL and comfortable with large, distributed data platforms (e.g., Snowflake, SQL Server, AWS RDS).
• Experience with identity resolution and entity matching using deterministic and probabilistic techniques.
• Experience building recommendation or personalization systems, including content-based and/or hybrid methods and cold-start strategies.
• Experience developing predictive models for customer behavior (churn, propensity, next-best-action, or similar).
• A practical understanding of real-time vs. batch serving and the latency considerations that shape model design.
• Familiar with production-ML concepts—containerization, API-based serving, and orchestration—and able to collaborate with ML Ops and Engineering to ship.
• Able to turn ambiguous objectives into clear, data-driven approaches and executable plans.
• A clear communicator who can tailor technical explanations to different audiences.
• A background in P&C insurance, subscription or membership businesses, or financial technology is a plus.

Qualifications

• Master's degree (or equivalent practical experience) in Data Science, Computer Science, Engineering, Mathematics, or a related quantitative field.
• 3+ years of hands-on machine learning and data science experience, including models deployed to production.
• Direct experience with a Customer Data Platform (CDP) and activation/audience workflows.
• Experience with graph modeling or knowledge graphs applied to customer or relationship data.
• Familiarity with our production toolset, or close equivalents:
• Docker or Podman for containerization
• SageMaker Endpoints or FastAPI for model serving
• Metaflow or Airflow for workflow orchestration
• Exposure to anomaly detection, embeddings, or feature stores supporting real-time use cases
• Experience owning a meaningful slice of the lifecycle—from research through deployment and monitoring—in partnership with ML Ops or platform teams.

Skills

• Python
• scikit-learn
• XGBoost
• SQL
• Modern ML frameworks
• Identity resolution and entity matching
• Recommendation and personalization systems
• Predictive modeling
• Real-time vs. batch serving
• Production-ML concepts
• Containerization
• Model serving
• Workflow orchestration
• Anomaly detection
• Embeddings
• Feature stores

Benefits

This position is open to U.S. remote work. However, team members who reside within 20 miles of the Traverse City headquarters will follow a hybrid schedule, working from the office three days per week. May require travel for quarterly events. Familiarity with public company requirements, including Sarbanes Oxley and key regulations, if applicable.

Pay

Compensation, comprehensive benefits and the perks that set us apart.

Schedule

U.S. remote work. However, team members who reside within 20 miles of the Traverse City headquarters will follow a hybrid schedule, working from the office three days per week. May require travel for quarterly events.