AI Analytics Product Manager

Job Description

AI Analytics Product Manager

eBay is looking for an AI Analytics Product Manager to lead the shift from traditional dashboards and reporting toward agentic, AI-native analytics products. This role sits at the crossroads of product, data science, and applied AI, and is responsible for turning eBay's product and business signals into diagnostics teams can actually act on — not just look at.

Instead of maintaining static reports, you'll own the roadmap for AI agents that watch product health, explain why a metric moved, and recommend what to do next. If you're excited about moving past analysis-for-its-own-sake and into shipping intelligence as a product, this role is built for you.

What You'll Own

• End-to-end product strategy for AI agents that monitor product health — performance, operational stability, user behavior, and core business metrics — across eBay's product portfolio.
• Workflows that let teams self-serve answers in plain language: ask a question about a metric, an anomaly, or a trend, and get a grounded, explainable answer back instead of a static chart.
• The translation of observability and monitoring needs into agent workflows — alert triage, root-cause exploration, metric drill-downs, and automated summarization — that scale across products and international markets.
• Cutting time-to-diagnosis on critical benchmarks by shipping AI-powered detection, explanation, and recommendation capabilities, in partnership with AI/ML and data engineering teams.
• The evaluation framework behind the product: defining trusted metrics, source-of-truth systems, and evidence standards so every AI-generated answer is grounded, explainable, and auditable — not a black box.
• Guardrails for high-stakes decisions — accuracy thresholds, explainability requirements, and human-in-the-loop review — so the organization can trust AI-assisted diagnostics enough to act on them.

What Makes This Role Different

This isn't a BI-to-PM lateral move. You're productizing intelligence — deciding what an agent should investigate on its own, what it should escalate to a human, and how it explains its reasoning. Success looks like teams trusting an AI agent's root-cause explanation enough to skip three dashboards and act directly on it.

Requirements

• Roughly 12+ years of experience in data science, analytics, or adjacent fields, with hands-on work building or contributing to AI analytics, observability, monitoring, or decision-support products.
• Direct experience designing or managing AI-first or LLM-enabled products — chatbot-style or agentic experiences, prompt flows, retrieval strategies, and feedback loops for continuous model/product improvement.
• Working fluency in product health metrics, anomaly detection, and root-cause analysis workflows, ideally at large-scale product or platform companies.
• Comfort operating across product analytics platforms, observability tooling, and experimentation systems — this role lives in the space between dashboards, experiments, and data pipelines.
• Demonstrated collaboration with AI/ML and analytics teams on RAG, evaluation methodology, grounded answer quality, and AI safety — with a genuine focus on transparency and auditability, not just shipping speed.

Preferred

• A track record of turning ambiguous stakeholder needs into a clear, prioritized product direction — this role requires translating "we don't trust this number" into a concrete roadmap item.
• Experience prioritizing features by weighing usage patterns, qualitative feedback, and business impact against engineering effort.
• Prior exposure to large e-commerce, marketplace, or platform environments where product signals span multiple markets and business units.