Senior Principal AI Product Manager - GTM Analytics

Palo Alto Networks

3d•$216,000 - $298,000•Onsite

About The Position

We are looking for a visionary, hands-on Senior Principal Product Manager to drive GTM Analytics, fundamentally transforming how insights are generated and consumed in an increasingly AI-driven world. In this pivotal role, you will be responsible for defining the GTM Analytic strategy & roadmap and guiding execution alongside a skilled team of Data Engineers. You will redefine how Palo Alto Networks uses data to drive Go-to-Market (GTM) excellence. You are not a middleman for requirements; you are a Technical General Manager who owns the "AI-First" transformation of our sales and marketing intelligence. Keeping pace with the latest AI advancements, you will leverage your domain expertise to conceptualize and develop agentic data products to solve for GTMs problem spaces. Furthermore, you will serve as the GTM Subject Matter Expert for Data Governance establishing rigorous processes to ensure freshness of the data catalog and the quality of critical data elements.

Requirements

Bachelor’s degree with 15+ years of relevant experience, Master’s degree with 12+ years of relevant experience, or PhD with 8+ years of relevant experience.
Proven track record of managing the end-to-end delivery of data and analytics products from concept through launch at an enterprise level.
Expert ability to deconstruct complex challenges, formulate hypotheses, execute data-driven analysis, and translate findings into practical, actionable recommendations.
Demonstrated proficiency in analytics across the Customer Journey, including an expert understanding of processes and key performance metrics for Lead-to-Opportunity conversion, Sales Forecasting, Pipeline Coverage and Quality, and Post-sales tracking of Customer Value, Renewals, and Expansion rates.
Hands-on experience developing functional prototypes and fully functioning MVPs using AI coding tools such as Cursor.
Extensive hands-on experience working with SQL and data visualization tools.
Solid understanding of Large Language Model (LLM) capabilities, including concepts such as context windows, RAG, and memory.

Nice To Haves

Knowledge of Python.

Responsibilities

Develop, maintain, and prioritize the GTM Analytics roadmap, strategically balancing immediate wins with long-term initiatives.
Translate business needs and stakeholder requests into clear, actionable technical epics.
Define and own the Product Requirements Document (PRD) for strategic GTM projects, ensuring rigorous standards for metric definition, data quality, and monitoring.
Analyze GTM data to share strategic insights with GTM leadership on a regular basis.
Act as the core liaison between business leadership and the engineering team, transforming GTM strategy into concrete execution plans and effectively managing delivery commitments across cross-functional teams.
Drive the cultural shift from "requesting a report" to "interacting with an agent." You will lead the internal GTM community in adopting AI-driven workflows and insights.
Champion the use of the proprietary Natural Language AI data analyzer.
Lead the onboarding of GTM datasets, continuously refining the business context layer with golden queries and rigorously tested Evals to address high-value business scenarios.
Quickly build and iterate on functional prototypes using AI-native tools to validate concept and user experience fit.
Represent the data track in vertical AI initiatives, ensuring key data signals are readily available in workflows to boost user productivity and drive superior business outcomes.
Develop agentic MVPs in coding tools such as Cursor to rapidly accelerate time to deploy AI enabled solutions.
Utilize the in-house, proprietary AI tool to convert Business Requirements into actionable Product Requirements and User Stories.
Be a change agent, promoting the evolution from static dashboards to personalized views enhanced by AI recommendations and natural language querying.
Own the centralized data catalog for all GTM metrics and critical data elements.
Partner closely with Data Stewards and IT Engineering teams to strategically inspect, prioritize, and manage data quality remediation efforts.