Senior Enterprise Data & AI/ML Product Lead

Hagerty; United States; On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Remote work options

Hybrid schedule near Traverse City HQ

Travel for quarterly events

Job summary

Hagerty seeks a Group Technical Product Manager for Enterprise Data, Analytics, and AI/ML to own strategy and delivery across the data value chain. You will lead a small team of TPMs responsible for enterprise data platforms, ML models, generative AI applications, data pipelines, and BI.

This player-coach role requires staying close to delivery, shaping long-term platform vision with engineering, data science, and business stakeholders, and ensuring data governance and compliance across

Qualifications

• 8+ years of product management experience, with significant experience in enterprise data, analytics, ML, or AI-focused roles.
• 3-5+ years of people management experience, including coaching and developing TPMs or similar roles.
• Proven track record leading enterprise data and AI product portfolios from raw data ingestion through ML models, generative AI features, and BI consumption.
• Hands-on familiarity with the full data product lifecycle: enterprise data platforms, pipelines, model development, feature engineering, deployment, monitoring, and iteration.
• Strong understanding of LLM application patterns and the product challenges of building reliable, safe AI-driven experiences.
• Working fluency in outcome-driven prioritization (Jobs to Be Done, Kano), economic sequencing (cost of delay, WSJF), capability roadmapping (layered roadmaps, Wardley mapping), and probabilistic delivery forecasting sufficient to coach TPMs against Hagerty's product plan review standard.
• Strong technical acumen with the ability to engage meaningfully with data engineers, data scientists, ML engineers, and software engineers.
• Excellent communication and stakeholder management skills, with the ability to influence at senior and executive levels.
• Insurance or financial services industry experience is a plus.

Responsibilities

• Own the strategy and roadmap across Enterprise Data, Analytics, and AI/ML enterprise data platforms, data pipelines, ML models, generative AI, and BI ensuring alignment with company objectives.
• Lead, coach, and develop a small team of TPMs covering Enterprise Data, Analytics, and AI/ML fostering high performance and a strong ownership culture.
• Ensure consistent, high-quality execution across enterprise data platform delivery, ML model productionization, AI feature rollout, and BI development.
• Produce and maintain the portfolio-level roadmap architecture mission, product/system, and technology layers aggregating Enterprise Data, Analytics, and AI/ML domain roadmaps into one coherent view with figures of merit and technology-readiness levels per initiative.
• Act as the senior product voice for Enterprise Data, Analytics, and AI/ML across Engineering, Data Science, Operations, and business leadership.
• Identify what is blocking portfolio outcomes including platform capacity, governance, or data engineering priorities outside your direct line and influence leadership to resolve it.
• Communicate portfolio strategy, delivery progress, risks, and tradeoffs clearly at all organizational levels from sprint review to executive briefing.
• Establish and evolve product management processes, backlog standards, and delivery practices across Enterprise Data, Analytics, and AI/ML.

Skills

Product management

Leadership

Stakeholder management

Technical acumen

Data governance knowledge

Tools

Snowflake

Databricks

Azure AI services

Azure ML

ML orchestration tools

Job description

Hagerty seeks a Group Technical Product Manager for Enterprise Data, Analytics, and AI/ML to own strategy and delivery across the data value chain. You will lead a small team of TPMs responsible for enterprise data platforms, ML models, generative AI applications, data pipelines, and BI.

This player-coach role requires staying close to delivery, shaping long-term platform vision with engineering, data science, and business stakeholders, and ensuring data governance and compliance across