Staff Product Manager, Data Platform and Machine Learning

The Staff Product Manager is an expert product practitioner who represents mastery of product management craft while developing readiness for product leadership responsibilities. They operate with exceptional autonomy across multiple teams, act as mentors to other product team members, and serve as thought partners to senior leadership while maintaining independent judgment. They create frameworks that drive organizational decision-making and establish systems for identifying emerging opportunities that scale beyond individual observation.

You will report to the Director of Product for Data and Post-Bind Journeys (financial systems, premium audit, and claims) and work closely with Data Engineering leadership, Data Science teams, and cross-functional partners to identify opportunities, prioritize initiatives, and drive end-to-end execution from conception to impact measurement.

Mission For This Role

The Staff Product Manager for Data & ML will lead three critical teams in Pie's data ecosystem, ensuring that our data infrastructure serves as a strategic advantage that powers Pie's ability to make superior insurance decisions through data-driven insights and machine learning capabilities.

How you’ll do it 

Data Architecture:

Our Data Architecture team defines core data concepts, their relationships, business logic, and governance processes across the enterprise. You'll enhance our enterprise data warehouse (EDW2) by establishing strong data contracts between operational systems and data pipelines, ensuring consistency and reliability as systems evolve. You'll develop frameworks for data quality management and governance that scale with our business, creating a trustworthy foundation for analytics and decision-making.

Data Platform:

The Data Platform team builds the plumbing that moves data from operational systems to our analytical environment. You'll lead the development of resilient ETL pipelines using tools like Snowflake and Airflow, prioritizing reliability, observability, and efficiency. You'll oversee the transition from legacy data sources to system-generated data, reducing manual intervention and increasing automation. Your deep technical fluency in SQL and data engineering concepts will be crucial for guiding architectural and product decisions and implementing best practices.

DataOps for ML/AI:

Our DataOps for ML/AI team (Data Chefs) creates curated data layers that power machine learning model development, training, and deployment. You'll build the infrastructure that accelerates our ML capabilities using platforms like AWS SageMaker. A key focus will be developing the data foundation that enables faster iteration on our proprietary pricing models, connecting claims outcomes, underwriting decisions, and pricing inputs into a continuous and deep learning system. You'll also establish evaluation frameworks for assessing external AI capabilities against internal development options.

Key Outcomes For First 12 Months:

• Develop a comprehensive data strategy that balances technical excellence with tangible business impact, clearly articulating how improved data capabilities will drive Pie's competitive advantage in the mid/high risk workers comp and commercial auto segments.
• Identify key areas to invest in more durable data contracts between operational systems and data pipelines, reducing pipeline failures while accommodating the ongoing evolution of our systems.
• Establish and execute a strategy for progressively reducing reliance on manual/spreadsheet-based data sources, with clear priorities based on business impact and technical feasibility.
• Create a measurement framework for data quality, pipeline reliability, and ML model performance that enables data-driven decision making across the organization.
• Develop the data infrastructure to accelerate pricing model optimization cycles, focusing on curated data layers that connect claims outcomes, underwriting decisions, and pricing inputs.
• Successfully integrate the Data & ML domains into Pie's Product Operating Model, adapting ceremonies and workflows to accommodate the unique characteristics of data products while maintaining agile principles.
• Build strong, collaborative relationships with technology and business leaders across Pie, establishing yourself as a trusted advisor on data strategy and ML implementation through both technical expertise and business acumen.
• Contribute to an enhanced data governance strategy that includes working with compliance and security teams to ensure data meets regulatory and industry standards.

The Right Stuff

• 10+ years overall professional experience working on complex business and/or technology problems.
• 7+ years Product Management experience working in an agile software development lifecycle (SDLC) environment.
• Advanced SQL proficiency is required, with hands-on experience writing complex queries for data analysis and transformation.
• Extensive technical knowledge of modern data warehousing solutions (e.g., Snowflake), workflow orchestration tools (e.g., Airflow), data extraction services (e.g. Fivetran), ML platforms (e.g., AWS SageMaker) and data quality tools (e.g. Monte Carlo). 
• Demonstrated ability to assess technical limitations and constraints, make appropriate tradeoffs, and communicate these decisions effectively to both technical and business stakeholders.
• Strong understanding of data modeling, data governance, and analytics best practices, with experience implementing these in complex organizational environments.
• Experience with establishing a data-driven culture by educating teams on best practices for data stewardship and data governance.
• Expertise with data accuracy & consistency initiatives, with experience building scalable data validation frameworks to ensure clean and reliable data for data science modeling, analytical insights and statutory reporting. 
• Ability to translate complex data science and data engineering solutions into business value for non-technical stakeholders with supporting metrics to measure data/model accuracy, business impact and adoption.
• Strategic thinking combined with tactical execution skills, allowing you to develop a long-term vision while delivering incremental value through short-term wins.
• Deep intellectual curiosity and demonstrated ability to rapidly build domain expertise in complex industries, with insurance experience being valuable but not required.