Principal Data Scientist - Knowledge Engineering, Customer Success Data Platform

Intuit is building a knowledge and agentic intelligence foundation for Customer Success. The Principal Data Scientist will define and own the knowledge architecture and governance for the ICS data platform and agentic intelligence layer, guide a technical team, and contribute hands-on to building and prototyping the platform.

Responsibilities:

• Own the agentic knowledge architecture. Define the target architecture for how ICS knowledge is modeled, stored, connected and served to agents and people. This covers the Intuit Customer Success Knowledge Graph, ontologies and taxonomies, entity resolution, and how structured data, unstructured content and expert knowledge come together
• Architect the agentic intelligence layer. Set the patterns for how agents retrieve, reason over and act on knowledge. This includes GraphRAG and hybrid retrieval (graph, vector and structured queries), context engineering, tool and MCP design, multi-agent orchestration, and memory
• Set the semantic layer strategy. Define how business metrics, entities and definitions are expressed once and reused across dashboards, data agents and AI experiences. The goal is that “what does this number mean” has one answer everywhere
• Lead data and knowledge governance. Build the governance strategy for knowledge quality, freshness, lineage, provenance, access control and responsible AI. Make sure agents answer from trusted, compliant and well-attributed sources
• Make the key architecture and tooling decisions. Evaluate and select graph stores, vector stores, semantic layer tools, agent frameworks and evaluation tooling. Make clear build-versus-buy calls and document the tradeoffs
• Build hands-on. Prototype reference implementations, write production-quality code, review designs and pull requests, and step into the hardest technical problems yourself
• Balance now and next. Deliver what the business needs this quarter, such as tax-season readiness and high-impact self-serve use cases. Make sure each delivery moves toward the long-term architecture instead of adding technical debt
• Define how we measure quality. Set the evaluation frameworks for knowledge coverage, retrieval quality, answer accuracy, groundedness and agent reliability, and use them to drive prioritization
• Translate between business and technology. Work with segment data leaders, business, engineering, and operations to understand their workflows, nuances and decisions. Explain architectural choices in business terms and build alignment across teams
• Guide and grow the team. Mentor data scientists, technical data analysts, and engineers. Raise the technical bar through design reviews and standards, and build a strong knowledge engineering practice across ICS