Principal Product Manager

Remote · Waltham · USD 151,900-238,700 · 8+ years experience · Full-time · Remote · Posted Sep 14, 2026

At a glance

ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of
your life. You'll be surrounded by teammates who care deeply, challenge each other, and celebrate wins.
With tools that amplify your impact and a culture that backs your ambition, you won't just contribute, you'll
make things happen, fast.
The Opportunity
ZoomInfo's Core Data team builds and maintains the person and company data that powers the Go-To-

ZoomInfo is hiring a Principal Product Manager to own Person Data outcomes end to end and build the AI evaluation discipline for the Core Data team.
This role drives the pivot from rule-based selection to agent-adjudicated records for hundreds of millions of contact records.
The person in this role will prototype adjudication agents, evals, and analysis in code while partnering closely with engineering and data science.

Own Person Data end to end by setting the strategy, roadmap, and monthly priorities for contact data accuracy, coverage, freshness, and compliance.
Build and run the AI evaluation engine by defining correct attributes, curating golden sets and trap records, standing up LLM-as-judge gates, and setting confidence thresholds.
Drive the pivot from rules to agents by moving person attributes from deterministic selection to inference over full evidence, writing policy clauses, grading guides, and acceptance rules.
Bring AI into the pipeline without tanking data by introducing models where they earn their place while keeping classical tooling for normalization, string similarity, registry lookups, and deterministic rules.
Treat entity resolution as the hard core by managing person-to-company matching, wrong-company links, duplicate people, and identity across sources with a clear mental model for canonical identity and match confidence.
Own the privacy roadmap for person data including suppression, opt-out, and notification with Legal and Privacy.
Build with the team hands-on by prototyping adjudication agents, evals, and analysis in code with the AI engineer and data science, and committing to the shared repository.
Drive cross-functional execution and communicate strategically as the hub between Person Data engineering, Match, Research, Data Science, Web Data acquisition, Privacy, and application teams.

8+ years of product management experience, with meaningful time owning data products or data platforms at scale.
Hands-on experience with data pipelines, ideally incremental or streaming rather than batch rebuilds, with a working understanding of how records move from ingestion through processing to a served dataset.
Direct experience with entity resolution, record linkage, or matching systems including canonical IDs, match confidence, survivorship rules, and trade-offs between precision and recall.
Demonstrated, current use of AI to improve data systems, including bringing an LLM into a production data process and describing how accuracy was kept from degrading.
Experience designing evaluations for model output such as golden sets, precision and recall, LLM-as-judge, human-in-the-loop routing, and production drift monitoring.
Comfort working in code to read a pipeline, write a prompt and a validator, run an analysis in SQL or Python, and commit alongside engineers.
Experience partnering closely with engineering and data science on complex, data-intensive systems, and a track record of influencing technical direction without formal authority.
Strong bias for action and a demonstrated ability to drive focus and finish work in a fast-moving, ambiguous, and frequently chaotic environment.
Excellent written and verbal communication with the ability to move between technical depth and executive narrative.

Experience with contact, person, or identity data, or with B2B company data
Familiarity with privacy regulation as it applies to 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