Data Product Manager - AI and Analytics

Data Product Manager

• Define and drive product vision, strategy, and execution for AI-driven user enablement, spanning natural language insights, analytics experiences, developer productivity tools, and enterprise chatbots.
• Partner with engineering, data science, and platform teams to deliver scalable and secure solutions across cloud and enterprise ecosystems.
• Lead user discovery and research across multiple personas (business associates, plant engineers, analysts, developers) to ensure solutions address real pain points.
• Translate user needs into epics, PRDs, and evaluation benchmarks; prioritize features and enhancements using impact-driven frameworks.
• Build and maintain success metrics around adoption, accuracy, usability, and performance to guide continuous improvement.
• Advocate for governance, metadata quality, and knowledge management to ensure AI-driven tools produce accurate and trusted results.
• Communicate product strategy and progress to senior leadership and stakeholders, influencing adoption and organizational alignment.

• User Adoption: Measurable growth in unique active users across business and developer personas.
• Accuracy & Trust: ≥ 80% accuracy on benchmarked AI responses, with high user satisfaction scores.
• Productivity Impact: Demonstrable improvements in developer and analyst productivity through AI-driven automation and insights.
• Scalability: New teams/domains onboarded with reduced friction and improved metadata quality.

• 8–10 years of Product Management experience, ideally with data, analytics, AI/ML, or developer tools.
• Proven success in AI/ML-enabled products, with strong understanding of large language models (LLMs), NLP, and conversational interfaces.
• Strong grasp of cloud technologies, enterprise data platforms, and modern analytics ecosystems.
• Demonstrated ability to work across cross-functional teams (engineering, ML, UX, business stakeholders) to drive end-to-end delivery.
• Excellent skills in user research, stakeholder engagement, and storytelling with data.
• Experience setting KPIs and evaluation frameworks for AI/analytics products.
• Strong written and verbal communication skills; able to engage both executive and technical audiences.

Experience Level

Mid Level