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Analytics is undergoing a fundamental shift—from traditional business intelligence and data science toward AI-native, conversational analytics products .
We are building a new generation of analytics experiences where business and product teams can interact with data through natural language, diagnose performance, uncover root causes, and take action with the support of AI agents.

We are looking for a technical, product-minded Data Scientist / AI practitioner to help build these experiences, with a particular focus on analytics chatbots, AI orchestration, retrieval and grounding, agentic workflows, and evaluation .

If you are motivated to move beyond traditional analysis into building intelligent systems that combine LLMs, enterprise data, analytical context, and business workflows, this is an prospect to help shape how eBay interacts with data in the AI era.

About The Role
In this role, you will design and develop the intelligence layer behind AI-powered analytics chatbots and agentic analytics products .





You will work across the end-to-end conversational analytics stack—including LLM orchestration, intent understanding, retrieval, context engineering, tool calling, SQL/data access, analytical reasoning, response generation, and evaluation .

A key part of the role will be designing systems that can translate natural-language questions into reliable analytical workflows: identifying the right data and business context, retrieving relevant knowledge, invoking analytical tools, reasoning across results, and returning grounded and explainable answers.

You will partner closely with Analytics, Data Engineering, Product, and Engineering teams, as well as Legal, Privacy, Security, and Responsible AI, to move AI capabilities from experimentation into reliable enterprise products.

What You’ll Do
Build Conversational & Agentic Analytics Systems

Develop AI-powered analytics chatbots and conversational interfaces that allow users to explore business performance using natural language.

Design multi-step agentic workflows that can interpret user intent, retrieve context, query data, perform analytical reasoning, and generate grounded responses.

Build orchestration flows connecting LLMs with SQL engines, analytics APIs, semantic layers, knowledge bases, and other analytical tools.





Design tool-calling patterns and agent routing strategies that determine which data sources, analytical workflows, or specialized agents should handle a request.

Develop structured response patterns supporting analytical outputs such as narratives, tables, charts, follow-up questions, and recommended actions.

Translate ambiguous analytical questions into deterministic and AI-assisted workflows that are reliable enough for enterprise decision-making.

Design the Retrieval & Context Layer

Build retrieval architectures across structured and unstructured analytics knowledge, including metric definitions, schemas, SQL examples, dashboards, analytical documentation, historical analyses, and business context.

Design RAG and context-engineering workflows using techniques such as semantic search, embeddings, metadata filtering, hybrid retrieval, reranking, and dynamic context assembly.

Develop strategies for retrieving structured context—including relevant schemas, tables, dimensions, metrics, and example SQL—to support accurate data querying.

Optimize knowledge organization, chunking, deduplication, indexing, and retrieval strategies for analytical use cases.

Design mechanisms to maintain and continuously update domain‑specific analytics knowledge as definitions, schemas, and business logic evolve.

Develop AI Orchestration & Analytical Reasoning

Design LLM orchestration pipelines spanning intent classification, planning, retrieval, tool selection, query generation, execution, reasoning, and response synthesis.

Develop single-agent and multi-agent architectures, including specialized agents for different

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📌 AI Analytics Manager (Ontario)
🏢 eBay
📍 Ontario