Principal Product Manager, Person Data and AI Evaluations
Principal Product Manager owning ZoomInfo's person/contact data quality end to end while building the AI evaluation engine (golden sets, LLM-as-judge, precision/recall gates) as Core Data pivots from rule-based selection to agent-adjudicated records. Hands-on role expecting daily AI use, SQL/Python analysis, and code commits.
Compensation
$151.9K–$238.7K / yr
Location
Not listed
Work setup
Remote
Seniority
Principal
Employment
Not specified
Experience
8+ years
Category
Product
Posted
Skills
agentic-ai
ai
automation
ccpa
data-engineering
data-pipelines
data-quality
data-science
gdpr
go-to-market
llm
python
sql
zoominfo
Must have
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
Direct experience with entity resolution, record linkage, or matching systems: canonical IDs, match confidence, survivorship rules, precision/recall trade-offs
Demonstrated, current use of AI to improve data systems, including bringing an LLM into a production data process without degrading accuracy
Experience designing evaluations for model output: golden sets, precision and recall, LLM-as-judge, human-in-the-loop routing, production drift monitoring
Comfort working in code: read a pipeline, write prompts and validators, run analysis in SQL or Python, commit alongside engineers
Experience partnering with engineering and data science on complex data-intensive systems and influencing technical direction without formal authority
Strong bias for action and ability to drive focus and finish work in a fast-moving, ambiguous environment
Excellent written and verbal communication, able to move between technical depth and executive narrative
Nice to have
Experience with contact, person, or identity data, or with B2B company data
Familiarity with privacy regulation as applied to published personal data (GDPR, CCPA, DNC, 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
Full description
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.
Principal Product Manager, Person Data and AI Evaluations
ZoomInfo | Product | Core Data
About ZoomInfo
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- Market Intelligence Platform: hundreds of millions of contact records, resolved to the right person at the right
company, kept accurate, and delivered to more than 35,000 customers.
That data has always been produced by deterministic pipelines: take many competing sources, weight them,
decay them, and select a winner per attribute. That model is being replaced. Core Data is moving to an
inference-default operating model, where an agent reads the full body of evidence for a record, applies our
business policy, proposes the answer, and a deterministic verification layer decides whether it lands.
Selection logic goes away; verification and evaluation are what remain. The product manager's job changes
with it. Instead of writing requirements for hand-built selection rules, you run the evaluation engine that
decides whether the agent's output is good enough to publish, and you make it better every week.
We are hiring a Principal Product Manager to own Person Data outcomes end to end and to build the AI
evaluation discipline that the whole Core Data team will run on. You inherit a live portfolio: person data
quality (removing bogus or outdated executive contacts, unlinking contacts from the wrong company, title
classification, email deliverability), person coverage and extraction, and the privacy roadmap for person data.
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.
What You'll DoOwn Person Data end to end. Set the strategy, roadmap, and monthly priorities for ZoomInfo's contact data: 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.
What You'll Bring
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,
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.
Preferred
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
Who You AreAI-native, not AI-curious. You use AI tools every day and you have opinions about where inference beats 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.
The Environment
You report to the Senior Manager of Product for Core Data and sit inside the Core and Global Data 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?
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.
Below is the US base salary for this position. Additional compensation such as Bonus, Commission, Equity and other benefits may also apply.
$151,900—$238,700 USD
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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