Principal Data Scientist - Knowledge Engineering, Customer Sucess Data Platform (AI)

Location: Mountain View or San Diego, California, United States

Salary: $247,500–$335,000 annual base pay in Mountain View

Company

hirify.global is a financial technology platform providing products including TurboTax, Credit Karma, QuickBooks, and Mailchimp.

What you will do

• Own the knowledge architecture and governance for the hirify.global Customer Success data platform and knowledge graph, including ontologies, taxonomies, entity resolution, and integration of structured, unstructured, and expert knowledge.
• Architect the agentic intelligence layer using GraphRAG, hybrid graph/vector/structured retrieval, context engineering, tools, MCP, multi-agent orchestration, and memory.
• Define the semantic layer and metrics strategy so business definitions remain consistent across dashboards, data agents, and AI experiences.
• Establish governance for knowledge quality, freshness, lineage, provenance, access control, compliance, and responsible AI.
• Evaluate graph and vector stores, semantic-layer tools, agent frameworks, and evaluation tooling; make build-versus-buy decisions and document tradeoffs.
• Prototype reference implementations, write and review production code, define evaluation frameworks, translate business needs into architecture, and mentor data scientists, analysts, and engineers.

Requirements

• 15+ years in data and knowledge engineering or AI and ML engineering, including significant experience as a technical lead or architect on large-scale data or knowledge platforms.
• Production experience architecting and shipping large-scale data and knowledge systems, such as enterprise knowledge graphs, semantic platforms, or data platforms serving many teams and use cases.
• Deep expertise in ontology and taxonomy design, entity resolution, graph data modeling, knowledge graph construction, and using LLMs for extraction and enrichment.
• Hands-on experience building LLM-powered and agentic systems, including RAG, GraphRAG, hybrid retrieval, LangGraph, LlamaIndex, MCP, context engineering, and agent evaluation.
• Deep knowledge of AI infrastructure, including vector and graph databases such as Neo4j or Neptune, embedding and retrieval pipelines, model serving, and cloud data platforms such as Databricks, Snowflake, or AWS, including scale, latency, and cost tradeoffs.
• Experience defining semantic-layer and metrics strategies, data and knowledge governance, lineage, provenance, quality, freshness, access control, and responsible AI practices.
• Strong programming skills with the ability to write and review production code, plus a pragmatic approach to balancing short-term business needs with long-term architecture.
• Excellent communication and ability to influence without authority across business, engineering, and operations.
• Experience mentoring and technically guiding data scientists, data professionals, and engineers.
• BS in Computer Science, Data Science, Statistics, Information Science, or a related field.

Nice to have

• Knowledge systems experience in customer success, customer support, or contact-center environments, including agent assist, knowledge-base optimization, or self-service.
• Experience building natural-language-to-SQL or self-serve data agents over enterprise warehouses.
• Familiarity with RDF, OWL, SHACL, SPARQL, and property graphs using Cypher.
• Experience with LLM evaluation and observability tooling such as Langfuse or MLflow, and evaluation-driven development.
• Experience in regulated domains such as finance or tax.
• Publications, patents, open-source contributions, or conference talks in knowledge graphs, semantic AI, or agentic systems.
• MS or PhD.

Culture & Benefits

• Competitive compensation with a pay-for-performance approach.
• Potential eligibility for cash bonus, equity rewards, and benefits under applicable plans and programs.
• Pay is determined by job-related knowledge, skills, experience, and work location.
• Regular pay comparisons are conducted across ethnicity and gender categories to support fair pay.