Sr Principal Product Manager - Agentic/AI Data Collection

Job Summary

The Senior Principal Product Manager is a seasoned product professional responsible for high-impact, high-visibility internal platforms. Senior Principal Product Managers engage with internal stakeholders and process owners to find the right problems to solve, define what success looks like, and guide their team to deliver successful outcomes. They identify solutions that are valuable to internal users, viable for the business, feasible to build, and usable.

Senior Principal Product Managers typically coordinate several agile delivery teams on large and strategically important products. They are responsible for driving all aspects of the product development lifecycle - from insight-driven discovery and strategy to defining success metrics, planning, execution, and continuously optimizing to deliver value.

The Senior Principal PM has a product portfolio mindset and is always looking to direct resources toward the most valuable problems to solve. They take on an increasingly visible role in advocating for product management best practices - in this role, applied to building and scaling an AI/Agentic-native data collection pipeline and driving its adoption across all data collection processes.

What You’ll Do

30% Insights - Derives insights to support product bets

• Understands the dynamics of internal data collection processes, the roles and responsibilities of the teams that depend on them, and how AI/Agentic techniques are evolving in data extraction and collection. Builds this understanding through regular, direct research and engagement with internal stakeholders and process owners across data collection.
• Responsible for product discovery for the data collection pipeline. Proactively identifies opportunities across existing data collection processes and validates that a given solution is valuable, viable, feasible, and usable before it is developed - including evaluating build-vs-buy and where AI/Agentic techniques materially improve accuracy or efficiency.
• Uses data to evaluate and inform strategy and decision making, including performance under increasing data volume and internal user demand.
• Measures what matters: product success metrics (internal engagement, adoption across data collection processes, stickiness, extraction accuracy, coverage) as well as financial metrics (support costs, cloud/compute costs).

30% Strategy - Identifies and articulates a path to achieve results

• Defines product strategy - vision, value proposition, and roadmap - for the AI/Agentic data collection pipeline, in the context of departmental strategy, overall company strategy, and relevant firm type strategies.
• Evangelizes the product strategy across the business unit, including how the pipeline supports downstream document and data lifecycle capabilities.
• Responsible for product business case development (investment needs, resourcing, KPIs) to take the product from POC/prototype to a fully industrialized, production-ready solution.
• Accountable for adoption and retention goals across all data collection processes, as well as cost/efficiency goals.
• Partners with process owners and operations teams across data collection to drive validation and buy-in for the pipeline.
• Defines the adoption and rollout strategy for onboarding data collection processes onto the pipeline, including sequencing and prioritization.
• Creates a product roadmap that aligns cross-functional teams - collecting and prioritizing requirements from internal stakeholders and translating them into actionable milestones, including constraints and dependencies for scaling the pipeline.

25% Execution - Delivers on product outcomes

• Aligns engineering and operations teams to deliver on the vision, goals, and strategy for the data collection pipeline, coordinating the process to meet timelines.
• Plans releases, prioritizes features, and defines the work and what success looks like; clearly defines internal user needs and acceptance criteria.
• Interacts regularly with development teams to prioritize needs and provide clarity for iterating on solutions.
• Owns product scalability: ensures the AI/Agentic data collection solution remains stable, robust, scalable, and cost-efficient as data volume and internal usage grow, implementing proactive monitoring and collaborating with engineers to balance cost and performance.
• Owns integration and adoption: drives onboarding of data collection proces…