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Platform Product Manager, Ontology & Knowledge Graph

Vizient · Chicago, IL · $77,400–$135,400 a year · Full time

Posted Aug 3, 2026 · We last checked this listing on Oct 5, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

About your past

How would you explain to a business leader why an ontology and knowledge graph matter for AI products?

What they are checking: Whether they can make a semantic layer's value concrete in terms of consistent answers and faster product delivery.

Tell me about a time you owned a technical product where you had to learn the domain quickly.

What they are checking: Learning speed and humility working closely with engineers in a specialized area.

Tell me about a time you had to explain a delay to internal clients who depended on your platform.

What they are checking: Transparent communication and trust-building with internal stakeholders who depend on platform timelines.

What would you do

Two product teams define "supplier" differently, and both say theirs is right. How do you resolve it?

What they are checking: Driving shared definitions through facilitation and governance, which is the hard human part of taxonomy work.

How do you use AI tools to speed up discovery or requirements work, and how do you check their output?

What they are checking: Real AI-first product habits with discipline about accuracy in a precise semantic domain.

Metadata quality is poor in a key source system. How do you handle that as product owner?

What they are checking: Working with data owners on quality fixes while setting honest expectations for downstream users.

About the work

Walk me through how you'd model the relationships between suppliers, products, contracts and hospitals in a knowledge graph.

What they are checking: Practical modeling sense for entities, relationships and identifiers in Vizient's actual domain.

How do you decide what goes on the backlog for a semantic platform when the users are internal engineering teams?

What they are checking: Prioritizing platform capabilities by which downstream products and AI use cases they unblock.

How does a knowledge graph improve retrieval for an LLM application compared with plain vector search?

What they are checking: Understanding of grounding, entity resolution and relationship reasoning that make AI outputs more accurate.

How would you measure whether the knowledge platform is succeeding?

What they are checking: Choosing metrics like coverage, reuse across products and AI answer quality rather than graph size.

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The full job description

As published by Vizient.

When you’re the best, we’re the best. We instill an environment where employees feel engaged, satisfied and able to contribute their unique skills and talents while living and working as their authentic selves . We provide extensive opportunities for personal and professional development, building both employee competence and organizational capability to fuel exceptional performance through an inclusive environment both now and in the future. Summary: In this role, you will independently own defined capabilities within the Knowledge & Semantic Platform that enable AI-powered products and consistent intelligence delivery across the organization. You will partner closely with engineering and internal clients to define and evolve the operating layer beneath the platform, including ontology, knowledge graph, metadata, entity relationships, taxonomy, and semantic models. You will manage the product backlog and roadmap for assigned platform capabilities while working in an AI-first product delivery model, leveraging AI tools to accelerate discovery, requirements development, communication, validation, and delivery support. Responsibilities: • Own the product backlog and near-term roadmap for assigned Knowledge & Semantic Platform capabilities, including ontology, knowledge graph, taxonomy, metadata management, semantic modeling, and related platform services. • Conduct discovery with internal clients to understand business concepts, workflow needs, data relationships, AI use cases, and opportunities to improve semantic consistency and intelligence delivery. • Define product requirements, user stories, acceptance criteria, semantic models, entity relationships, metadata standards, prompt and context artifacts, and validation approaches that enable scalable AI-driven solutions. • Partner with engineering leads, architects, AI Delivery, data engineering, security, and design teams to translate business concepts into reusable semantic platform capabilities. • Balance delivery priorities across internal client needs, engineering capacity, architecture direction, governance requirements, data stewardship practices, and emerging AI-assisted development opportunities. • Collaborate with teams using agentic coding tools, AI-assisted development environments, and AI-supported testing, documentation, and validation workflows to improve delivery quality and speed. • Support implementation planning, release readiness, documentation, validation, and adoption activities while promoting consistent semantic standards and reusable platform capabilities. • Use metrics, operational insights, client feedback, and AI-supported synthesis to refine backlog priorities and continuously improve ontology, metadata quality, semantic relationships, and platform adoption. • Create guidance, demonstrations, communications, and enablement resources that help internal teams effectively leverage semantic platform capabilities, knowledge graph assets, and AI-first development practices. • Identify opportunities to improve metadata quality, semantic consistency, data stewardship, self-service, and reusable knowledge assets that enhance AI-enabled products and retrieval-augmented generation (RAG) capabilities. Qualifications: • Relevant degree preferred. • 2 or more years of relevant experience required. • Typically possesses 3–5 years of relevant experience in product management, product ownership, enterprise platforms, knowledge management, semantic technologies, AI-enabled products, or platform enablement. • Experience working with ontology, knowledge graphs, taxonomy, semantic modeling, metadata management, graph databases, or related knowledge management technologies preferred. • Experience partnering with engineering and data teams to translate business concepts into scalable semantic models, entity relationships, and reusable platform capabilities. • Familiarity with AI-enabled applications, retrieval-augmented generation (RAG), large language models, or other AI technologies that leverage structured knowledge and semantic data preferred. • Demonstrated experience using AI tools such as Codex/ChatGPT, Claude Code/Co-Work, GitHub Copilot/Copilot Studio, and emerging agentic coding environments to improve product discovery, requirements development, documentation, analysis, and communication. • Understanding of data stewardship, metadata governance, healthcare data concepts, and semantic consistency principles preferred. • Strong analytical, communication, collaboration, and stakeholder management skills with the ability to drive adoption, reuse, quality, and measurable business value across platform capabilities. Estimated Hiring Range: At Vizient, we consider skills, experience, and organizational needs in our compensation approach. Geographic factors may adjust the range estimate and hires typically fall below the top range. Compensation decisions are tailored to individual circumstances. The current salary range for this role is $77,400.00 to $135,400.00. This position is also incentive eligible. Vizient has a comprehensive benefits plan! Please view our benefits here: http://www.vizientinc.com/about-us/careers Equal Opportunity Employer: Females/Minorities/Veterans/Individuals with Disabilities The Company is committed to equal employment opportunity to all employees and applicants without regard to race, religion, color, gender identity, ethnicity, age, national origin, sexual orientation, disability status, veteran status or any other category protected by applicable law.

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