Senior Product Manager, AI & Data Science Products

🕒 August 13

🇺🇸 United States – Remote

💵 $160k - $190k / year

⏰ Full Time

🟠 Senior

📊 Data Scientist

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 19%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Crunchbase

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2007

☁️ SaaS

🤝 B2B

💰 $50M Series D on 2022-08

SaaS • B2B

Crunchbase is a subscription software platform that aggregates and delivers structured data, news, and predictive intelligence about private and public companies, funding rounds, acquisitions, leadership moves, and market activity. It provides paid products (Crunchbase Pro, Crunchbase Business), a marketplace, data licensing, and tools for investors, sales teams, and researchers to discover, track, and act on company intelligence.

📋 Description

• Own the strategy and roadmap for Crunchbase’s customer-facing AI data layer • Identify high-value opportunities for predictions, classifications, signals, and insights that improve customer decisions • Build a differentiated portfolio of AI data products • Partner with Foundational Data to determine whether 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 improve workflows and decisions • Rapidly test 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 • Determine when AI data products are 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 across 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

🎯 Requirements

• 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, confidence, and model drift • Ability to reason about probabilistic and imperfect data and define appropriate quality thresholds • Strong analytical skills and ability to balance customer value, quality, coverage, cost, and speed • Excellent customer discovery, communication, and cross-functional leadership skills • 3+ years of Product Management, Data Product Management, AI/ML Product Management, or comparable experience • Experience owning customer-facing data science products from problem definition through launch and ongoing monitoring • Experience partnering closely with Data Science and Engineering teams • Demonstrated experience taking products from customer discovery and experimentation through scaled adoption • Ability to define quality criteria that reflect customer needs and make informed quality and coverage tradeoffs • Experience with B2B SaaS, data products, APIs, intelligence platforms, or commercializing differentiated data preferred