Product Manager - Data

About Harmonic

Harmonic is the startup discovery engine.

It pains us to see great startup opportunities consistently go unfunded not because of lack of talent, but lack of signal.

We're fixing that by mapping the entire startup landscape and building the tools that put the right companies in front of the right people at the right time. Investors, corporate development, and GTM teams use us to find breakout companies before they're obvious and turn that discovery into action. Whether that's a check, an acquisition, partnership or new business.

We're growing revenue at a rate of 100%+ annually, with $30M in funding from Craft, Floodgate, and Sozo Ventures. We're committed to ensuring hundreds of billions of dollars find the right founders, so the innovations that matter most flourish. If that resonates, let's talk.

About the Team

The Data team builds the data the rest of Harmonic runs on. Every company, person, and investor a customer looks up, and everything Scout – our AI agent – reasons over, passes through this team first. When the data is right, a customer finds the company nobody else has noticed yet. When it's wrong, it's the first thing they notice.

We combine internet-scale collection with information shared directly by founders, data from partners across the innovation ecosystem, and insights we derive ourselves. Those sources are incomplete, frequently disagree, and constantly change. Our job is to reconcile them into a knowledge graph that captures who people are, what companies are doing, how they connect, and how those relationships evolve.

As our first Data PM, you’ll report to the Head of Product and work closely with engineering and data operations. You’ll own which gaps we close, which new information we pursue, and how we know the result is good enough for customers to act on. Your decisions will shape both what customers can discover in Harmonic and what our AI products can reliably do for them.

What You'll Do

• The data roadmap and its impact. Identify the data improvements that would most change what customers can find, evaluate or act on. Use customer research, usage, commercial opportunities and quality issues to decide where we invest—and what we defer.

• Our strategy for acquiring data. Decide when to collect, partner, ask users or derive information ourselves. Evaluate sources for their incremental value, reliability, cost and ability to stay current. Partner with our founder-facing team to make direct contributions useful to both founders and customers.

• How we represent the startup ecosystem. Work with engineering to reconcile conflicting evidence, resolve company and person identities, and model relationships and changes over time. Decide how uncertainty and provenance should inform what customers and Scout can rely on.

• Quality we can measure. Define evaluations for accuracy, coverage and freshness that reflect actual customer tasks. Establish baselines, set launch criteria and catch regressions. Know when an aggregate score hides a serious gap in a segment customers care about.

• New data capabilities, from idea to adoption. Lead projects such as mapping company–investor relationships over time, adding cap table and revenue data, and building AI agents to extract structured information from difficult sources. Scope useful first releases with engineering and work with product and GTM to get them into customers’ hands.

• A systematic response to data problems. Investigate issues raised by customers, internal teams and our auditing agents. Distinguish isolated errors from failures in collection, extraction, matching or modeling, and prioritize fixes that improve the dataset beyond a single record.

Who You Are

• You’ve owned a data product. You have 5+ years in product management, including recent responsibility for data products. You can explain how your decisions improved what customers could do, alongside the underlying data metrics.

• You can reason through messy real-world data. You’ve worked with company, people or financial data. Given conflicting information about a company, you know what questions to ask, how to investigate, and what fixing the problem across millions of records would require.

• You’re technically fluent and hands-on. You can work through ingestion, extraction, entity resolution and classification with engineers, and independently investigate data to test an assumption. You can make informed tradeoffs among accuracy, coverage, freshness, latency and cost.

• You know how to evaluate quality. You’ve built or owned evaluation frameworks and understand their limitations. You can explain how you chose a representative sample, established ground truth and decided whether an improvement was ready to ship.

• You make clear decisions under uncertainty. You can turn competing requests into a focused roadmap, explain why one investment matters more than another, and define a useful first release before every question is answered.

• You stay close to the people using the data. You’re comfortable investigating a customer’s workflow or a disputed record yourself. You follow through to understand whether what shipped actually solved the problem.

• Experience at a private-market data provider, or building data-intensive workflows for investors, is a plus. Early-stage B2B experience is helpful.