Senior Product Manager, AI & Data Science Products

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, dealmakers, and analysts make the right decisions. 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, dealmakers, 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. About The 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. What You’ll Do AI & Data Science Product Strategy 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. Customer Discovery & Product Development 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. Quality & Product Economics 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. Adoption & Monetization 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. What We’re Looking For 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. Education And Experience 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. Success in This Role Looks Like Crunchbase launches differentiated AI data products that customers value and competitors cannot easily replicate. AI creates valuable intelligence and coverage that would be impractical to produce through traditional data collection alone. Customers adopt these products because they improve real workflows and decisions. AI data products contribute measurably to adoption, retention, expansion, and revenue while meeting appropriate quality and trust standards. Non-Goals This is not an internal AI tooling or general AI feature role. This is not ownership of foundational data collection, sourcing, or operations. This is not ML research or data generation for its own sake. AI data must solve meaningful customer problems and create measurable value. Interview Process We use a structured interview process so every conversation has a distinct purpose and candidates are evaluated consistently against role-relevant evidence. Recruiter Prescreen — qualification and mutual fit. Confirm role basics, motivation, logistics, compensation alignment, and candidate priorities. Interview Round 1 — hiring-manager evidence interview. Evaluate the capabilities most predictive of success using consistent behavioral questions and anchored scoring. Interview Round 2 — work sample or functional deep dive. Explore the role’s most important on-the-job capabilities through a realistic, time-bounded discussion or exercise. Final Round — decision-gap interview. Assess any unresolved evidence required for a confident decision, such as cross-functional collaboration, judgment, leadership, or values in practice. #J-18808-Ljbffr Crunchbase