Senior Product Manager

You will carry that portfolio through the pivot from rule-based selection to agent-adjudicated records, and

you will define how we know the new system is right.

This is a role for someone who has shipped data pipelines and entity resolution at scale and who is already

using AI every day to make data systems better. You should be as comfortable reading a golden set and an

eval report as you are writing a roadmap, and you should be excited that our product managers commit code

and work alongside agents as a normal part of the job.

accuracy, coverage, freshness, and compliance. Own the outcomes and the metrics that prove them, from

cleaning up bogus executive contacts and verifying employment through leadership extraction and person

location. Partner with the Person Data product manager already on the team and with Privacy, Trust and

Identity engineering to deliver the roadmap.

Build and run the AI evaluation engine. Define what "correct" means for each attribute the agent emits.

Curate golden sets and trap records with our research team, stand up LLM-as-judge gates, set the confidence

thresholds that route a record to auto-accept, auto-reject, or human adjudication, and track precision, recall,

and drift in production. Every model or prompt change to the person pipeline ships through the gate you own.

Drive the pivot from rules to agents. Move person attributes, one at a time, from deterministic selection to

inference over full evidence. Write the policy clauses the agent follows, the grading guides research uses to

score it, and the acceptance rules the pipeline enforces. Decide the order, prove each step in shadow mode

against the gold set, and retire the legacy logic when the numbers say it is safe.

Bring AI into the pipeline without tanking the data. Introduce models where they earn their place and keep

classical tooling where it wins: normalization, string similarity, registry lookups, deterministic rules. Reason

explicitly about cost per row, latency, determinism, and auditability. Know when a bigger model is the wrong answer.

Treat entity resolution as the hard core. Person-to-company matching, wrong-company links, duplicate

people, and identity across sources are the problems that make or break contact data. Bring a clear mental

model for canonical identity, match confidence, and the cost asymmetry between a false merge and a missed match.

Own the privacy roadmap for person data. Own the privacy roadmap for person data — suppression, opt-

out, and notification — with Legal and Privacy.

Build with the team, hands on. Prototype adjudication agents, evals, and analysis in code with the AI

engineer and data science. Commit to the shared repository. Use Claude and agentic tooling daily to inspect

results, tune prompts, and verify output so that no single absence blocks an iteration cycle.

Drive cross-functional execution and communicate strategically. Act as the hub between Person Data

engineering, Match, Research, Data Science, Web Data acquisition, Privacy, and the application teams that

consume contact data. Represent the roadmap and its rationale clearly to product and data leadership, and

write the release notes and customer-facing narrative with Product Marketing.

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 through processing to a served dataset

Direct experience with entity resolution, record linkage, or matching systems: canonical IDs, match

confidence, survivorship rules, and the trade-offs between precision and recall

Demonstrated, current use of AI to improve data systems, not just AI as a feature: you have brought

an LLM into a production data process and can describe how you kept accuracy from degrading

Experience designing evaluations for model output: golden sets, precision and recall, LLM-as-judge,

human-in-the-loop routing, and production drift monitoring

Comfort working in code: you can 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.

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

hand-built logic and where it does not. You would rather over-index on AI fluency and learn our data than the reverse.

An entity-resolution thinker. You see a data quality problem and immediately ask which entity it is about,

what the canonical form is, and what evidence would settle it.

An evaluator by instinct. You do not ship a model change without a gold set, a threshold, and a plan for what

happens at ten times the volume. "We'll A/B test it" is not an eval plan.

A builder. You prototype, you read code, you commit. You are comfortable being the person who inspects

agent output at nine in the morning and tunes the prompt by ten.

Tenacious. ZoomInfo is fast, distributed, and messy. Many teams touch person data. You go find the people

you need, you drive to a decision, and you keep the team focused on the few things that matter while

everything else flies past.

One team. Honest, has the team's back, celebrates wins and losses together.

organization under the Chief Data Officer. Your day-to-day partners are Person Data engineering, the Match

team, the Research organization that produces golden records, Data Science, and the AI engineer building the

shared model-and-eval platform. Expect interesting problems: when is a "Vice President" an executive and

when is it a mid-level title? How do you unlink wrong-company contacts at scale without losing the right

ones? How do you prove an agent is more accurate than the rules it replaces, and how do you keep proving it after launch?

faster with AI-ready insights, trusted data, and advanced automation. Its solutions provide more than 35,000

companies worldwide with a complete view of their customers, making every seller their best seller.

ZoomInfo is proud to be an equal opportunity employer, hiring based on qualifications, merit, and business

needs.

Actual compensation offered will be based on factors such as the candidate’s work location, qualifications, skills, experience and/or training. Your recruiter can share more information about the specific salary range for your desired work location during the hiring process. We want our employees and their families to thrive.

In addition to comprehensive benefits we offer holistic mind, body and lifestyle programs designed for overall well-being. Learn more about ZoomInfo benefits here.

About us:

ZoomInfo (NASDAQ: GTM) is the Go-To-Market Intelligence Platform that empowers businesses to grow faster with AI-ready insights, trusted data, and advanced automation. Its solutions provide more than 35,000 companies worldwide with a complete view of their customers, making every seller their best seller.

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ZoomInfo is proud to be an equal opportunity employer, hiring based on qualifications, merit, and business needs, and does not discriminate based on protected status. We welcome all applicants and are committed to providing equal employment opportunities regardless of sex, race, age, color, national origin, sexual orientation, gender identity, marital status, disability status, religion, protected military or veteran status, medical condition, or any other characteristic protected by applicable law. We also consider qualified candidates with criminal histories in accordance with legal requirements.

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