below marketmost similar roles pay hereabove market
This role pays more than
98%
of similar roles. Most pay
$143,551–$208,800
— the blue band above.
At the midpoint, this role pays about
$291k
versus about
$176k
for comparable roles.
Based on 240 similar postings.
Employer
About Intuit
Intuit is a financial software company known for products like TurboTax, QuickBooks, Mint, and Credit Karma, helping consumers and small businesses manage their finances and taxes. Industry: Financial Software & Technology
Intuit currently has
255 open roles
on FindRole.
Listed pay typically runs
$202,500–$274,000
across 236 roles with salary data.
The Principal Data Scientist - Knowledge Engineering, Customer Success Data Platform joins the Intuit Customer Success team to architect the knowledge and agentic intelligence foundation. This role involves designing the knowledge architecture, governance, and agentic intelligence layer for the Customer Success data platform, specifically the Customer Success Knowledge Graph. You will build systems for internal agent experiences and self-serve data agents that process plain language queries. Key responsibilities include developing ontologies, taxonomies, and entity resolution while implementing GraphRAG, hybrid retrieval, and multi-agent orchestration. You will utilize technologies such as Neo4j, Neptune, Databricks, Snowflake, AWS, LangGraph, and LlamaIndex. The work focuses on creating a semantic layer for consistent business metrics and ensuring responsible AI through rigorous data governance, lineage, and provenance in a regulated environment.
What does a Data Scientist earn in California?
Median $214500 from 117 postings across 27 companies.
Define and own the knowledge architecture, ontologies, and taxonomies for the ICS data platform.
Architect the agentic intelligence layer, including GraphRAG, hybrid retrieval, and multi-agent orchestration.
Set the semantic layer strategy to ensure consistent business metrics across all AI experiences and dashboards.
Build a governance strategy for knowledge quality, lineage, provenance, and responsible AI practices.
Evaluate and select core infrastructure tools including graph stores, vector stores, and agent frameworks.
Write production-quality code to build prototypes and solve the hardest technical problems.
Establish evaluation frameworks to measure knowledge coverage, retrieval quality, and agent reliability.
Mentor data scientists and engineers while raising the technical bar through design reviews and standards.
What we're looking for
15+ years in data and knowledge engineering, or AI and ML engineering, including significant time as a technical lead or architect.
Track record of architecting and shipping large-scale data and knowledge systems in production, such as enterprise knowledge graphs or semantic platforms.
Deep expertise in knowledge modeling, including ontology and taxonomy design, entity resolution, graph data modeling, and knowledge graph construction.
Hands-on experience building LLM-powered and agentic systems, including RAG, GraphRAG, hybrid retrieval, and agent frameworks.
Deep knowledge of AI infrastructure, including vector and graph databases, embedding pipelines, model serving, and cloud data platforms.
Experience defining semantic layer and metrics strategy, data and knowledge governance, and strong programming skills to write production code.
Experience mentoring and technically guiding teams of data scientists, data professionals, and engineers.
BS in Computer Science, Data Science, Statistics, Information Science or a related field (MS or PhD preferred).