AI Analytics PM BI to AI Transformation

eBay•San Jose, CA

1d•Remote

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. Analytics is undergoing a fundamental shift, transitioning from traditional data science to analytics product development passionate about intelligent systems. 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 help reimagine our Business Intelligence ecosystem as AI-native applications. You will take on 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 develop how eBay interacts with data in the AI era, this is an opportunity to be at the forefront of that journey.

Requirements

10+ years of Data Science and Analytics experience.
Expert in Business Intelligence tools including Tableau and/or Google Looker.
Product management perspective.
Experience building and scaling AI-powered products, including LLM, conversational Analytics, and AI workflows.
Ability to define platform-minded product strategies that support reuse across multiple domains and teams.
Solid understanding of analytical workflows, user needs, and how data-grounded AI products should behave.
Experience working closely with Engineering on APIs, platform capabilities, integrations, and operational scale.
Comfort defining requirements for timely response quality, grounded answers, retrieval, evaluation, and trust.
Strong partner management and communication skills across technical and non-technical groups.
Ability to prioritize across short-term product wins and long-term platform investments.

Nice To Haves

Experience with RAG, evaluation systems, AI feedback loops, or AI product governance.
Experience launching products across multiple business domains or customer segments.
Exposure to Responsible AI, privacy, security, or regulatory considerations in AI products.

Responsibilities

Lead the planning for incorporating Business Intelligence tools into AI applications across multiple domains, use cases, and business functions.
Design uniform procedures for AI diagnostics tool creation across the whole Analytics organization.
Prototype and direct the advancement and scaling of new AI diagnostics and conversational Analytics functions.
Specify how AI agents can assist in diverse analytical workflows, including exploration, reporting, diagnosis, and decision support.
Construct reusable product templates that facilitate AI capabilities extending across teams, domains, and customer demands.
Work closely with cross-functional Analytics teams, Legal, Privacy, Security, and Responsible AI to provide scalable, dependable AI products.
Define the product vision and roadmap for scaling AI analytics products across multiple domains and user journeys.
Identify high-value opportunities where AI agents can improve speed, clarity, trust, and decision quality.
Build repeatable product patterns for chat-based analysis, guided investigation, summarization, recommendations, and self-serve insights.
Partner with AI and engineering teams on platform capabilities such as prompt orchestration, tool use, RAG, evaluation, and feedback systems.
Drive product requirements for domain onboarding, knowledge integration, quality controls, and transparent user experiences.
Define success metrics across adoption, answer quality, trust, latency, and business impact.
Balance innovation with safety, governance, privacy, and policy requirements across different domains.
Align team members across product, engineering, analytics, and domain teams around a unified AI product strategy.