Product Manager - Semantic Data & AI Platform

GEICO

Palo Alto (CA)

On-site

USD 95,000 - 158,000

Full time

14 days+

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Job summary

GEICO is seeking a customer-obsessed Product Manager to drive the development of an enterprise semantic and ontology layer that standardizes metrics and enables trusted analytics across BI tools, data products, and AI experiences.

The role sits at the intersection of product management, data platform, analytics, and AI, translating complex requirements into intuitive platform capabilities to improve data trust and self-service analytics for enterprise data domains.

Qualifications

5+ years of product management experience building data, analytics, AI, or internal platform products.
Strong understanding of data modeling, business metrics, semantic layers, BI concepts, or governed analytics experiences.
Experience translating complex business and technical requirements into clear product requirements, roadmaps, and user-focused capabilities.
Proven ability to work closely with engineering, data, analytics, governance, and business stakeholders on technically complex products.
Working knowledge of AI/ML and generative AI concepts, including LLM capabilities and limitations, natural language query, prompt design, evaluation, and responsible use of AI in governed data environments.
Experience using AI-powered tools to boost product management productivity while adhering to data privacy, security, and governance requirements.
Excellent communication, stakeholder management, prioritization, and problem-solving skills.

Responsibilities

Define and drive the product vision and roadmap for a semantic and ontology layer enabling self-service analytics, BI, and AI use cases.
Translate business needs into standardized metric definitions, semantic models, and domain concepts scalable across enterprise data domains.
Partner with engineering, data architecture, and analytics teams to deliver governed semantic layer capabilities.
Support natural language analytics and AI-powered data experiences grounded in trusted semantic context.
Collaborate with stakeholders to validate metric definitions and prioritize high-value use cases.
Define and track product success metrics including adoption, consistency, discoverability, and AI answer quality.
Drive adoption of semantic and ontology capabilities across BI tools, data products, and analytics workflows.