AI Analytics Product Manager

eBay•San Jose, CA

About The Position

At eBay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts. Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet. Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all. AI Analytics Product Manager Analytics is undergoing a fundamental shift, from traditional data science toward AI-driven analytics product development. As business intelligence evolves into intelligent, AI-powered tooling, we are looking for a Data Scientist eager to help lead this transformation. This role sits at the intersection of analytics, product, and AI. You will play a key part in reimagining our Business Intelligence ecosystem as a suite of AI-native applications, stepping into the role of an AI Analytics Product Manager. We are building toward a future where business and product teams can diagnose performance, uncover insights, and take action through AI-enabled experiences. If you’re motivated to move beyond analysis into productizing intelligence and want to shape how eBay interacts with data in the AI era, this is an opportunity to be at the forefront of that journey.

Requirements

12+ years of Data Science and Analytics experience
Experience in developing AI Analytics Products, observability, monitoring, or decision-support products.
Strong understanding of KPIs, weekly performance diagnostic, product health metrics, anomaly detection, and root-cause analysis workflows.
Experience building AI-first or LLM-enabled products, including chatbot-style experiences.
Ability to define product requirements for prompt flows, grounded answers, retrieval, and feedback loops.
Strong problem solving and analytical skills with comfort working across metrics, experimentation, dashboards, and data workflows.
Strong communication skills and ability to translate ambiguous user needs into clear product direction.
Experience with product analytics platforms, observability tools, or experimentation systems.

Nice To Haves

Familiarity with RAG, evaluation frameworks, prompt iteration, and AI safety considerations.
Experience designing products for alerting, incident analysis, or operational decision-making.
Exposure to regulated or policy-sensitive environments where explainability and auditability matter.

Responsibilities

Lead the Analytics product strategy for agentic AI experiences focused on product health and performance diagnostics.
Build AI-powered workflows that help teams detect issues, explain metric changes, surface root causes, and recommend actions.
Partner with AI Developers, Data Science Analytics and domain teams to turn complex product signals into trusted, actionable insights.
Drive the roadmap for monitoring agents that improve visibility into product performance, operational health, user behavior, and business metrics.
Define the vision, roadmap, and success metrics for AI agents focused on product health monitoring.
Build experiences that let users ask natural-language questions about KPIs, anomalies, trends, regressions, and drivers.
Translate monitoring needs into agent workflows, including alert investigation, metric analysis, root-cause exploration, and summarization.
Partner with AI/ML teams on prompt strategy, RAG, evaluation, and grounded answer quality.
Work with analytics and data teams to define trusted metrics, source systems, and evidence requirements.
Define guardrails for accuracy, explainability, escalation, and human review in high-impact decision workflows.
Prioritize features using user feedback, usage patterns, business impact, and operational reliability.
Ensure outputs are transparent, actionable, and grounded in trusted data sources.