Principal Product Manager - Person Data

You'll own the strategy for ZoomInfo's person data, which is hundreds of millions of contact records used by more than 35,000 customers. We're moving from rule-based selection to AI agents that read the full evidence for each record, and you'll build the evaluation engine that decides whether their answers are good enough to publish. If you've shipped data pipelines and entity resolution at scale and use AI every day to improve data systems, you'll help define how we prove the new system is right.

What You'll Do

• Own Person Data end to end, setting the strategy, roadmap, and monthly priorities for the accuracy, coverage, freshness, and compliance of contact data.
• Build and run the AI evaluation engine: define "correct" for each attribute, curate golden sets and trap records, stand up LLM-as-judge gates, and set confidence thresholds that route records to auto-accept, auto-reject, or human review.
• Drive the pivot from rules to agents by moving person attributes from deterministic selection to inference over full evidence, proving each step in shadow mode, and retiring legacy logic when the numbers support it.
• Introduce models where they earn their place and keep classical tooling where it wins, weighing cost per row, latency, determinism, and auditability.
• Solve entity resolution challenges, including person-to-company matching, wrong-company links, and duplicate people.
• Own the person data privacy roadmap (suppression, opt-out, and notification) in partnership with Legal and Privacy.
• Prototype adjudication agents, evals, and analyses in code alongside the AI engineer and data science team, and commit to the shared repository.
• Partner across Person Data engineering, Match, Research, Data Science, Privacy, and application teams, and present the roadmap and its rationale to product and data leadership.

What You Bring

Must-Have

• 8+ years of product management experience, including meaningful time owning data products or data platforms at scale
• Hands-on experience with data pipelines, ideally incremental or streaming rather than batch rebuilds, and a working understanding of how records move from ingestion to a served dataset
• Direct experience with entity resolution, record linkage, or matching systems, including canonical IDs, match confidence, survivorship rules, and precision/recall trade-offs
• Current, demonstrated use of AI to improve data systems, including bringing an LLM into a production data process while keeping accuracy from degrading
• Experience designing evaluations for model output, including golden sets, precision and recall, LLM-as-judge, human-in-the-loop routing, and production drift monitoring

Preferred

• Experience with contact, person, or identity data, or with B2B company data
• Familiarity with privacy regulation for published personal data (GDPR, CCPA, DNC, and international notification regimes)
• Experience with Claude or comparable frontier models, agent frameworks, and eval harnesses
• Background at an AI research lab or an AI-native data company

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