{"id":3413609,"url":"https://alion.io/job/intuit-principal-data-scientist-knowledge-engineering-customer-success-data-platform","title":"Principal Data Scientist - Knowledge Engineering, Customer Success Data Platform","company":{"id":16628,"name":"Intuit","domain":"intuit.com","url":"https://alion.io/company/intuit","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Radancy","truth_index":{"grade":"B","score":78,"open_postings":56,"ghost_share":0,"stale_share":0.857,"repost_share":0.018,"time_to_fill_p50_days":40,"computed_at":"2026-10-29T05:49:05Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mountain View, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":247500,"max":335000,"currency":"USD","period":"year","gross":null,"usd_annual":335000},"salary_estimate":null,"experience_years_min":15,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon Neptune","optional":false},{"name":"AWS","optional":false},{"name":"Context Engineering","optional":false},{"name":"Databricks","optional":false},{"name":"GraphRAG","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"MLFlow","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Neo4j","optional":false},{"name":"RAG","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"LangChain","optional":true}],"status":"live","first_seen_at":"2026-10-27T00:00:00Z","employer_posted_date":"2026-10-27","last_verified_at":"2026-10-29T02:49:24Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Intuit Customer Success is building the knowledge and agentic intelligence foundation that will power how millions of customers get help and how our teams make decisions based on knowledge and data. We’re looking for a hands-on Principal Knowledge Engineer to be the architect of that data and knowledge foundation.\nYou will define and own the knowledge architecture and governance for the ICS data platform and agentic intelligence layer, including the Intuit Customer Success Knowledge Graph. Your work will power use cases such as internal customer success agent experiences and self-serve data agents that let teams ask questions in plain language and get trusted answers. This is a rare chance to set the architecture and system for an enterprise-scale agentic intelligence platform from the ground up. You will guide a team of data scientists, data professionals, and engineers, and you will stay close enough to the code to build, prototype and unblock.\nResponsibilities\nOwn 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.\n\nArchitect 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.\n\nSet 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.\n\nLead 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.\n\nMake 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.\n\nBuild hands-on. Prototype reference implementations, write production-quality code, review designs and pull requests, and step into the hardest technical problems yourself.\n\nBalance 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.\n\nDefine 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.\n\nTranslate 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.\n\nGuide 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.\n\nQualifications\nRequired\n15+ years in data and knowledge engineering, or AI and ML engineering, including significant time as a technical lead or architect on large-scale data or knowledge platforms.\n\nA track record of architecting and shipping large-scale data and knowledge systems in production, such as enterprise knowledge graphs, semantic platforms, or data platforms that serve many teams and use cases.\n\nDeep expertise in knowledge modeling, including ontology and taxonomy design, entity resolution, graph data modeling and knowledge graph construction. This includes using LLMs for extraction and enrichment.\n\nHands-on experience building LLM-powered and agentic systems, including RAG and GraphRAG, hybrid retrieval, agent frameworks (for example LangGraph, LlamaIndex, MCP), context engineering and agent evaluation.\n\nDeep knowledge of AI infrastructure: vector and graph databases (for example Neo4j, Neptune), embedding and retrieval pipelines, model serving, cloud data platforms (for example Databricks, Snowflake, AWS), and their scale, latency and cost tradeoffs.\n\nExperience defining semantic layer and metrics strategy so that business definitions stay consistent across BI and AI consumers.\n\nExperience designing data and knowledge governance, including lineage, provenance, quality, freshness, access control and responsible AI practices.\n\nStrong programming skills and the ability to write and review production code.\n\nA pragmatic approach to architecture: you can meet short-term business needs with flexible solutions while steadily building toward a long-term vision.\n\nExcellent communication and the ability to influence without authority. You understand people, process and business needs, and you can speak the business team’s language as fluently as the engineering team’s.\n\nExperience mentoring and technically guiding teams of data scientists, data professionals and engineers.\n\nBS in Computer Science, Data Science, Statistics, Information Science or a related field. An MS or PhD is preferred.\n\nPreferred\nExperience with knowledge systems for customer success, customer support or contact-center environments, such as agent assist, knowledge-base optimization or self-service.\n\nExperience building natural-language-to-SQL or self-serve data agents over enterprise warehouses.\n\nFamiliarity with semantic web standards (RDF, OWL, SHACL, SPARQL) alongside property graphs (Cypher).\n\nExperience with LLM evaluation and observability tooling (for example Langfuse, MLflow) and evaluation-driven development.\n\nExperience in regulated domains such as finance or tax, where accuracy, provenance and compliance are critical.\n\nPublications, patents, open-source contributions or conference talks in knowledge graphs, semantic AI or agentic systems.\n\nIntuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.\n The expected base pay range for this position is:Mountain View $247,500 - $335,000","description_format":"text","description_chars":6762,"description_truncated":false,"requirements":{"experience_years_min":15,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Personal Finance Apps & Education","Accounting & Tax Software","Tax Advisory & Preparation"],"lifecycle":[{"event":"open","at":"2026-10-28T18:39:34Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 719 · green card filings: 199","filings_12m":719,"filings_prev_12m":718,"green_card_filings_12m":199,"median_offered_wage_usd":177081,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)","US Department of Labor: PERM disclosure data (green cards)"],"filings_for_role_12m":71}],"liveness":{"score":63,"band":"ok","label":"Likely open","p_open":1,"p_active":0.632,"p_room":1,"age_days":2,"expected_fill_days":40,"reasons":["conf:2","stale_co","velocity","win:early","comp:brand"],"computed_at":"2026-10-29T05:49:05Z"},"pay":{"stated_usd_annual":335000,"is_top_pay":true},"html_url":"https://alion.io/job/intuit-principal-data-scientist-knowledge-engineering-customer-success-data-platform","json_url":"https://alion.io/job/intuit-principal-data-scientist-knowledge-engineering-customer-success-data-platform.json","meta":{"generated_at":"2026-10-29T06:18:45Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":9580,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-30T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":16628},"rest":"https://alion.io/mcp/rest/get_company?id=16628"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fintuit-principal-data-scientist-knowledge-engineering-customer-success-data-platform"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fintuit-principal-data-scientist-knowledge-engineering-customer-success-data-platform"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fintuit-principal-data-scientist-knowledge-engineering-customer-success-data-platform"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/intuit-principal-data-scientist-knowledge-engineering-customer-success-data-platform\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fintuit-principal-data-scientist-knowledge-engineering-customer-success-data-platform"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3413609}},{"tool":"search_jobs","arguments":{"company":"intuit.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3413609},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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