Senior Product Manager, AI & Data Science Products

Senior Product Manager, AI & Data Science Products

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.

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.

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. Assess any unresolved evidence required for a confident decision, such as cross-functional collaboration, judgment, leadership, or values in practice.