Sr. Product Manager - Web Personalization (Agentic Web)

Microsoft's mission is to empower every person and every organization on the planet to achieve more. In Consumer Marketing, we bring that mission to life by transforming how customers discover, compare, and buy Windows and Surface devices on the web.

We are seeking anAI Discovery + Web Personalization PM (Agentic Web Lead)to define and scale the next generation of Microsoft's consumer web experience, where discovery and journeys are increasingly shaped by AI systems. This role owns the end-to-end product strategy that connects three components into one cohesive system:

1. AI discovery readiness: ensuring our content is accurately understood, selected, and cited by answer engines and AI assistants

1. Session-based personalization: adapting modules and pathways in near real time based on customer signals and intent

1. Interactive agent experiences: enabling on-site assistants that guide discovery, comparison, education, and handoffs with trust and governance

The ideal candidate blends product management, technical AEO fluency, experimentation rigor, and strong cross-functional leadership. You can turn emerging "agentic web" behaviors into tangible platform requirements, measurable roadmaps, and scaled operating mechanisms across global experiences.

• Own the Agentic Web strategy and operating model: Define the annual vision and roadmap across AI discovery, personalization, and on-site agent journeys, with clear intake, prioritization, and operating rhythms to sustain optimization at scale.

• Run AI discovery (AEO) end to end:Set standards for answer-ready content and machine interpretability, and continuouslymonitor, troubleshoot, and evolve performance with SEO, content, and engineering as answer engines change.

• Launch and manage web personalization as a product:Own the full lifecycle from requirements and launch through ongoing roadmap delivery, tuning, and continuous improvement based on business priorities and customer signals.

• Ensure personalization platform reliability:Define SLAs, instrumentation, monitoring, and quality gates (signal freshness, latency, delivery health), and drive incident response and post-incident improvements with engineering and analytics.

• Productize andoperateinteractive agent experiences:Define agent vision and requirements, then own post-launch iteration of intent capture, journey handoffs, escalation paths, and recommendation quality through measurable learning loops.

• Build andmaintainthe intent and content model:Steward the unified intent layer, taxonomy, metadata, and module mapping that connects discovery signals, in-session behavior, and agentcontextso the system stays scalable and governable.

• Lead measurement and experimentation for long-term lift:Own dashboards and performance reviews, run an ongoing test-and-learn cadence to prove incrementality, scale winners, retire underperformers, and align cross-functional teams on actions and outcomes.

• Champion trust, safety, and governance:Embed privacy, accessibility, accuracy, provenance, and brand-voice guardrails into both launches and ongoing operations, ensuring the systemremainscompliant and trustworthy as content and models evolve.