Senior Product Manager, AI & Data Science Products
Listed on 2026-08-22
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IT/Tech
Data Analyst, AI Engineer (Applied/Software), Data Scientist, Machine Learning/ ML Engineer
About Crunchbase
Crunchbase is a predictive solution that provides intelligence on private companies, powered by the unique combination of live private company data, AI, and market activity from over 80 million users. We predict private market movements that matter to help investors, deal makers, and analysts make the right decisions.
About CrunchbaseCrunchbase is a predictive solution that provides intelligence on private companies, powered by the unique combination of live private company data, AI, and market activity from over 80 million users. We predict private market movements that matter to help investors, deal makers, and analysts make the right decisions.
We are committed to fostering a positive, diverse, and inclusive culture by hiring for potential and embracing individuals with diverse perspectives, backgrounds, experiences, and skill sets. We value transparency and openness, believing that an inclusive environment strengthens our teams and enhances our products.
AboutThe Role
The Senior Product Manager, AI & Data Science Products owns Crunchbase’s customer-facing AI data layer: proprietary data and intelligence generated from foundational data using AI and machine learning.
The primary charter is to identify high-value opportunities for new model-derived data, validate their value with customers, and take successful products from experimentation through scaled adoption.
Success Is Measured By Three Outcomes- New differentiated data:
Create proprietary intelligence that Crunchbase could not practically produce through collection alone. - Higher customer value:
Help customers discover, understand, evaluate, and prioritize their private market jobs more effectively. - Revenue and adoption:
Turn valuable AI data into measurable usage, retention, expansion, and monetization opportunities.
- Own the strategy and roadmap for Crunchbase’s customer-facing AI data layer.
- Identify high-value opportunities for new predictions, classifications, signals, and insights that improve customer decisions.
- Build a differentiated portfolio of AI data products rather than isolated AI features.
- Partner with Foundational Data to determine when customer needs are best addressed through collected, acquired, inferred, predicted, or generated data.
- Work directly with customers to identify where new or better data can materially improve their workflows and decisions.
- Rapidly test new AI data concepts, validate customer value, and scale successful products.
- Define how model-derived data, including confidence and uncertainty, should be presented to customers.
- Partner with Design, Engineering, and Data Science to deliver AI data across Crunchbase products, APIs, MCP, and data delivery experiences.
- Define quality standards and evaluation frameworks for model-derived data in partnership with Data Science.
- Determine when an AI data product is sufficiently reliable for scaled customer use.
- Balance customer value, coverage, accuracy, freshness, and generation cost.
- Monitor product and data performance and continuously improve quality based on customer feedback and observed outcomes.
- Drive adoption of AI data products across Crunchbase’s customer experiences and distribution channels.
- Partner with Go-to-Market on positioning, customer education, and launch strategy.
- Partner with Pricing and Packaging and Sales to identify monetization opportunities.
- Measure adoption, retention, expansion, revenue, and customer outcomes to determine which products to scale, improve, or retire.
- Strong product judgment across customer discovery, strategy, prioritization, experimentation, and tradeoffs.
- Strong understanding of data products and how customers derive value from proprietary data and insights.
- Practical understanding of modern machine learning and AI capabilities and limitations.
- Working knowledge of applied data science and machine learning.
- Ability to translate product requirements for Data Science and Engineering teams.
- Familiarity with model evaluation concepts such as precision, recall,…
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