AI/ML Product Manager/Lead (Enterprise Data)

Requirements

• Typically requires 12+ years of related experience of enterprise Tech AI product management, including 5+ years in SaaS or Data/AI platforms,
• Hands-on data expert: Have deep domain expertise mastering datasets and metrics. Analyze data using (Python, SQL) and build solutions and prototypes,
• Strong Technical background in Applied AI/ML, Data Engineering, Data Science,
• Proven experience building and managing GenAI/LLM capabilities with deep understanding of Applied AI into Enterprise Data,
• Experience with Agentic AI systems, autonomous agents, retrieval-augmented workflows, or orchestration frameworks,
• Experience with vector databases, LLMOps, data observability, and responsible AI practices,
• Deep expertise on Snowflake data platform, cloud, scalability, workload management, data-architecture and overall governance,
• Ability to make trade-offs between technical feasibility, user experience, and business impact,
• Exceptional communication and stakeholder management skills to translate complex AI concepts into actionable business specs with outcomes,
• Demonstrated success driving large, cross-functional stakeholder in ambiguous, high-growth environments,
• (Desirable) Understanding of ServiceNow platform is highly desirable,
• (Desirable) Having Figma expertise is a plus,
• The ideal candidate is a strategic thinker, fast learner, adept problem-solver, and obsessed about customer value

What the job involves

• The ServiceNow Enterprise Data team builds AI powered data solutions to unlock full value of Enterprise data. This means building capabilities to elevate the automation and experience to build AI-native applications and empowering everyone with Enterprise data to enable data-driven decision making for every persona in the organization,
• We are looking for an experienced Technical Product Manager for Enterprise Data team, part of central Data & Analytics team,
• With a focus on AI, this person will lead the strategy, roadmap, and execution of data and AI platform capabilities that power next-generation intelligent enterprise applications to enable Enterprise Data for every persona and unlock full value of Enterprise data,
• This role focuses on building scalable, trusted, and AI-ready data infrastructure, enabling seamless integration of GenAI and Agentic AI capabilities ensuring data orchestration, model lifecycle management, and observability,
• This is a technical product role and expects the candidate to bring in the hands-on expertise, ensuring enterprise-grade scalability, governance, and performance while delivering customer-centric, impactful solutions that accelerate AI adoption across the organization,
• This is a unique role to simplify the complexities for the data-world and create value with unlocking enterprise data yet keeping security as the key consideration,
• As we accelerate their AI transformation, the need for scalable, trusted, and data-rich platforms has never been greater,
• As a Sr Staff AI/ML Product Lead for Enterprise Data with a focus on Generative AI and Agentic AI, you will define and drive the solutions for AI-native data platform that power next-generation intelligent applications unlocking the full value of Enterprise data,
• Own the design, solution and execution for enterprise data and GenAI platform capabilities—focusing on data lifecycle management, prompt orchestration etc,
• Drive AI readiness and data initiatives, integrating governance, observability, and database capabilities into the data platform such as Snowflake,
• Collaborate closely with horizontal and vertical teams to embed AI-driven capabilities and experiences into key workflows, ensuring reusability, scalability, and trust across the ecosystem,
• Perform research, partner with x-functional teams, users and business leads, to understand the opportunities and ideate for enterprise grade design and solutions, with a focus on scale, quality, security, and self-service across the enterprise,
• Prototype and validate new Agentic AI and data-driven capabilities, translating early learnings into strategic product direction,
• Design solutions with clarity on how users will act and create value from the data and AI models; incorporates feedback loop instrumentation into the design,
• Drive the adoption of products amongst the user community, increase product satisfaction and drive ACV the impact from Analytics products,
• Combine technical depth with strategic focus, balancing innovation with operational excellence and performance optimization,
• Champion a customer-centric and data-driven culture, promoting a deep understanding of how data, AI models and intelligence come together to drive business outcomes,
• Influence across organizational boundaries, leading alignment between data, AI, and platform areas to ensure coherent architecture and shared innovation goals

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