Lead Data Scientist

As a Lead Data Scientist, you will serve as the technical bridge between our Data Science and ML Ops teams—driving innovation, scalability, and operational excellence. This is a hands-on senior individual contributor role with significant ownership and visibility across the organization.  

 A key early focus will be taking an existing R&D Generative AI system for transcribing and classifying call center operations, scaling it to production, and developing the technical roadmap to enhance and expand its capabilities—driving measurable improvements in customer experience, operational efficiency, and insight generation. In parallel, you will lead a comprehensive review of current and planned model deployments, providing strategic recommendations on the infrastructure and tools needed to ensure consistent, reliable, and scalable model performance; directly supporting the company’s ambitions for data-driven decision-making and service excellence. 

This role is ideal for someone who thrives on end-to-end ownership, enjoys solving complex technical challenges, and wants to shape the future of Hagerty’s Data Science capabilities.  

Ready to get in the driver’s seat? Join us. 

What you’ll do 

Modeling & AI System Development  

·Design, enhance, and productionize supervised ML and Generative AI models used in operational applications such as insurance conversion, lifetime value modeling, and loss avoidance.  

·Develop and deploy production-grade ML and LLM-based applications in Python, using scikit-learn, xgboost, langchain, and related frameworks.  

·Evaluate and refine LLM-driven workflows—covering retrieval, embeddings, performance monitoring, and Agentic System Design 

·Execute end-to-end modeling workflows: exploratory data analysis, feature engineering, model development, cross validation, and performance monitoring to ensure deployable, well-calibrated models. 

·Define and document Data Science best practices that support human and agentic coding tool collaboration—ensuring robust model standards, testing discipline, and maintainable workflows across the team. 

MLOps & Productionization  

·Lead the deployment and scaling of ML models using containerized services (Docker/Podman) and orchestration tools (Metaflow/Airflow). 

·Drive best practices for deploying ML models as FastAPI endpoints hosted in AWS.  

·Partner with MLOps teams to establish right sized infrastructure, reliable pipelines, automated testing, and performance monitoring for real-time ML applications.  

·Evaluate and continuously improve production systems for latency, reliability, and maintainability. 

Architecture, Data, and Technical Leadership 

·Conduct exploratory feature analysis using data from Snowflake, SQL Server, and AWS RDS Postgres, and partner with data engineering teams to transition proven features into scalable production pipelines. 

·Define and document architectural boundaries—clarifying component ownership, interfaces, and system dependencies across data, modeling, and application layers.  

·Translate business and research objectives into clear technical requirements, system designs, and execution plans that span from data ingestion to model deployment.  

·Collaborate closely with MLOps, engineering, and product teams to align data workflows, infrastructure, and APIs for reliable, maintainable production systems.  

·Ensure architectural integrity across the ML lifecycle—covering data lineage, reproducibility, version control, and system performance.  

·Lead technical discussions and reviews, mentoring team members in sound software design, modularization, and deployment practices.  

·Communicate architectural decisions, trade-offs, and impact to both technical and non-technical stakeholders, driving alignment and accountability. 

This Might Describe You 

·Deep experience designing, training, and deploying ML and Generative AI models in real-world applications.  

·Proficient in Python and modern ML frameworks such as scikit-learn, XGBoost, AWS Sagemaker/Bedrock, and LangChain.  

·Skilled in data analysis and feature exploration using SQL and distributed data platforms (e.g., Snowflake, SQL Server, AWS RDS).  

·Familiar with core concepts in production machine learning architectures, including containerization, API design, and orchestration frameworks; able to collaborate effectively with MLOps and engineering teams to bring models to production. 

·Adept at translating ambiguous business objectives into clear, data driven technical approaches and executable work plans.  

·Strong communicator who can synthesize complex technical concepts for a range of stakeholders, from engineers to executives.  

·Track record of driving technical quality through code and model reviews, paired development, and the documentation of best practices. 

·Applied experience with Generative AI—including LLMs, embeddings, RAG, finetuning, or agentic systems— in production or near production contexts 

Preferred  

·Master’s degree (or equivalent practical experience) in Data Science, Computer Science, Engineering, Mathematics, or a related quantitative field.  

·5+ years of hands-on machine learning and data science experience (developing and deploying models in production)  

·Experience with the following tools  

• Docker or Podman for containerization  
• Sagemaker Endpoints or FastAPI for model serving 
• Metaflow or Airflow for workflow orchestration. 

·Exposure to graph modeling, anomaly detection, or agent-based based methods.  

·Experience in roles where data scientists own the full lifecycle—from model research through deployment and monitoring—in partnership with MLOps or platform teams. 

Other things to note

• 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. For SOX compliant roles, responsible for designing, executing, and documenting internal controls where they have been identified as owners to prevent errors in financial reporting, processes, and business operations. Including attestation to the completeness, accuracy, and compliance of all financial reporting data, where applicable.

Say hello to Hagerty

Hagerty is an automotive enthusiast brand and the world’s largest membership organization. Along with being a best-in-class provider of specialty insurance for enthusiasts, Hagerty is also home to the Hagerty Drivers Foundation, Hagerty Drivers Club, Marketplace and so much more. Committed to saving driving for future generations, each and every thing Hagerty does is dedicated to the love of the automobile.

Hagerty is a rapidly growing company that values a winning culture. We provide meaningful work for and invest in every single team member.

At Hagerty, we share the road. We are an inclusive automotive community where all are welcomed, valued and belong regardless of race, gender, age, or car preference.  We are united by our shared passion for driving, our commitment to preserve car culture for future generations and our desire to make a positive impact in the world.

If you reside in the following jurisdictions: Illinois, Colorado, California, District of Columbia, Hawaii, Maryland, Minnesota, Nevada, New York, or Jersey City, New Jersey, Cincinnati or Toledo, Ohio, Rhode Island, Washington, British Columbia, Canada please email [email protected] for compensation, comprehensive benefits and the perks that set us apart. 

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If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!