Data Scientist, Tech Lead

Hagerty-posted about 24 hours ago

Full-time • Mid Level

Remote

1,001-5,000 employees

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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? Join us!

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.
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.
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.
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.

Master’s degree or higher degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field (or equivalent practical experience).
Extensive experience building and deploying ML models and GenAI systems in production environments.
Strong proficiency with Python and ML frameworks such as scikit-learn, XGBoost, and PyTorch.
Deep experience with SQL and data modeling in Snowflake, SQL Server, or similar distributed environments.
Hands-on experience building containerized applications and deploying using FastAPI, Docker/Podman, and workflow orchestration tools like Metaflow or Airflow.
Demonstrated ability to decompose ambiguous business problems into data-driven solutions.
Strong communication skills with an ability to simplify complex technical ideas for executives and business stakeholders.
Proven track record of mentoring other data scientists and delivering high-quality technical reviews.

Experience with generative AI (LLMs, embeddings, agents, RAG, fine-tuning) in a production or near-production context.
Experience with graph modeling, anomaly detection systems, or agent-based approaches.
Familiarity with automotive, insurance, or marketplace data.
Experience in environments where data scientists own both modeling and MLOps responsibilities (~50/50 split).

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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