Hagerty
201-500 employees
Insurance
Automotive
Financial Services
About Hagerty
Hagerty is a leading automotive lifestyle brand and insurance provider specializing in classic car insurance, enthusiast services, and automotive experiences. Founded in 1984, the company offers tailored insurance products for classic, collector, and specialty vehicles, along with a range of services including vehicle valuation, restoration advice, and community engagement for car enthusiasts. Hagerty's mission is to fuel the passion for automotive culture by providing expert insurance solutions and fostering a vibrant community of enthusiasts worldwide. The company has grown to become a trusted name in the classic car industry, combining insurance expertise with a deep appreciation for automotive heritage.
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Description
- As a Data Scientist, Tech Lead, you will operate as a technical leader on the Data Science team at Hagerty.
- This team supports machine learning and generative AI initiatives across the entire company—including insurance, online marketplaces, membership, and marketing.
- You will support this team as it independently designs, builds, and deploys high-impact models and AI systems.
- You will need to be equally comfortable developing supervised ML models, anomaly detection systems, agents, and generative AI applications, and you can take projects from conceptualization through production with limited guidance.
- You will serve as a mentor and reviewer for other data scientists, providing deep technical feedback on modeling approaches, code quality, and pull requests.
- You will influence the technical direction of the team by championing new methods, tools, and engineering practices.
- You must be comfortable frequently collaborating with engineering, MLOps, DevOps, business leaders, and making presentations to executive stakeholders.
- This role is ideal for someone who wants end-to-end ownership, broad impact, and the ability to shape the evolution of Hagerty’s data science capabilities.
- Ready to get in the driver’s seat?
- Modeling & AI System Development Plan, design, develop, and deploy end-to-end predictive, prescriptive, and generative models—including supervised ML, anomaly detection, agentic systems, and graph-based models.
- Build production-ready ML and GenAI applications using Python, scikit-learn, XGBoost, PyTorch, NetworkX, and related tools.
- Develop and evaluate LLM-driven applications, retrieval systems, embeddings, fine-tuning strategies, and agent workflows.
- MLOps & Productionization Own the full lifecycle of model deployment using Metaflow, Airflow, containerization (Podman/Docker), FastAPI, and related tooling.
- Collaborate with MLOps and DevOps teams to design scalable workflows, CI/CD pipelines, monitoring systems, and model governance processes.
- Monitor, evaluate, and remediate model performance in production environments.
- Data Engineering & Analysis Write high-quality code to query, transform, and analyze large datasets from Snowflake, SQL Server, and AWS RDS Postgres.
- Prepare modeling datasets, build feature pipelines, and ensure robustness, reproducibility, and data quality.
- Explore emerging technologies and methods to enhance data science capabilities across the organization.
- Leadership, Collaboration & Influence Lead full-scope projects independently from concept to delivery—setting timelines, identifying risks, and ensuring high-quality outcomes.
- Provide technical mentorship and thorough code/model reviews to other data scientists.
- Drive adoption of new libraries, tools, and engineering practices when appropriate.
- Collaborate closely with engineering, product, MLOps, DevOps, Marketing, and Insurance leaders.
- Present complex findings to both technical and non-technical audiences—including regular briefings to VP-level and C-suite stakeholders.
- Influence cross-functional alignment on new analytics approaches and model-driven solutions.
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Benefits
- Remote work within the U.S.
- Competitive salary and benefits package.
- Opportunities for professional growth and leadership.
- Collaborative and inclusive company culture.
- Familiarity with public company requirements, including Sarbanes Oxley and key regulations, if applicable.
- Travel for quarterly events may be required.