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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Product Manager, AI & Data Science Products - **Company:** Crunchbase, Inc. - **Location:** Denver, CO, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Machine Learning, Model Validation, B2b Software, Data Layers, Data Delivery, Data Generation - **Published:** August 13, 2026 - **Apply:** https://crunchbase.na.teamtailor.com/jobs/682445-senior-product-manager-ai-data-science-products ## About the Role * 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. ## Description 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 DoAI & 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., * 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. 1. Recruiter Prescreen - qualification and mutual fit. Confirm role basics, motivation, logistics, compensation alignment, and candidate priorities. 2. Interview Round 1 - hiring-manager evidence interview. Evaluate the capabilities most predictive of success using consistent behavioral questions and anchored scoring. 3. 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. 4. Final Round - decision-gap interview. 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