<div class="content-intro"><p>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.</p></div><div class="textLayer" style="text-align: left;" data-main-rotation="0"> <h3 style="text-align: left;"><strong>Principal Product Manager, Person Data and AI Evaluations</strong></h3> <p style="text-align: left;">ZoomInfo | Product | Core Data</p> <br><strong>About ZoomInfo</strong><br> <p style="text-align: left;">ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of</p> <p style="text-align: left;">your life. You'll be surrounded by teammates who care deeply, challenge each other, and celebrate wins.</p> <p style="text-align: left;">With tools that amplify your impact and a culture that backs your ambition, you won't just contribute, you'll</p> <p style="text-align: left;">make things happen, fast.</p> <br><strong>The Opportunity</strong></div> <div class="textLayer" style="text-align: left;" data-main-rotation="0"><br>ZoomInfo's Core Data team builds and maintains the person and company data that powers the Go-To- <p>Market Intelligence Platform: hundreds of millions of contact records, resolved to the right person at the right</p> <p>company, kept accurate, and delivered to more than 35,000 customers.</p> <p>That data has always been produced by deterministic pipelines: take many competing sources, weight them,</p> <p>decay them, and select a winner per attribute. That model is being replaced. Core Data is moving to an</p> <p>inference-default operating model, where an agent reads the full body of evidence for a record, applies our</p> <p>business policy, proposes the answer, and a deterministic verification layer decides whether it lands.</p> <p>Selection logic goes away; verification and evaluation are what remain. The product manager's job changes</p> <p>with it. Instead of writing requirements for hand-built selection rules, you run the evaluation engine that</p> <p>decides whether the agent's output is good enough to publish, and you make it better every week.</p> <p>We are hiring a Principal Product Manager to own Person Data outcomes end to end and to build the AI</p> <p>evaluation discipline that the whole Core Data team will run on. You inherit a live portfolio: person data</p> <p>quality (removing bogus or outdated executive contacts, unlinking contacts from the wrong company, title</p> <p>classification, email deliverability), person coverage and extraction, and the privacy roadmap for person data.</p> <p>You will carry that portfolio through the pivot from rule-based selection to agent-adjudicated records, and</p> <p>you will define how we know the new system is right.</p> <p>This is a role for someone who has shipped data pipelines and entity resolution at scale and who is already</p> <p>using AI every day to make data systems better. You should be as comfortable reading a golden set and an</p> <p>eval report as you are writing a roadmap, and you should be excited that our product managers commit code</p> <p>and work alongside agents as a normal part of the job.</p> <br><strong>What You'll Do</strong></div> <div class="textLayer" style="text-align: left;" data-main-rotation="0"><br><strong>Own Person Data end to end.</strong> Set the strategy, roadmap, and monthly priorities for ZoomInfo's contact data: <p>accuracy, coverage, freshness, and compliance. Own the outcomes and the metrics that prove them, from</p> <p>cleaning up bogus executive contacts and verifying employment through leadership extraction and person</p> <p>location. Partner with the Person Data product manager already on the team and with Privacy, Trust and</p> <p>Identity engineering to deliver the roadmap.</p> <br> <p><strong>Build and run the AI evaluation engine</strong>. Define what "correct" means for each attribute the agent emits.</p> <p>Curate golden sets and trap records with our research team, stand up LLM-as-judge gates, set the confidence</p> <p>thresholds that route a record to auto-accept, auto-reject, or human adjudication, and track precision, recall,</p> <p>and drift in production. Every model or prompt change to the person pipeline ships through the gate you own.</p> <br> <p><strong>Drive the pivot from rules to agents</strong>. Move person attributes, one at a time, from deterministic selection to</p> <p>inference over full evidence. Write the policy clauses the agent follows, the grading guides research uses to</p> <p>score it, and the acceptance rules the pipeline enforces. Decide the order, prove each step in shadow mode</p> <p>against the gold set, and retire the legacy logic when the numbers say it is safe.</p> <br> <p><strong>Bring AI into the pipeline without tanking the data</strong>. Introduce models where they earn their place and keep</p> <p>classical tooling where it wins: normalization, string similarity, registry lookups, deterministic rules. Reason</p> <p>explicitly about cost per row, latency, determinism, and auditability. Know when a bigger model is the wrong answer.</p> &l