Principal, AI Product Management, TruIQ

Trans Union's Job Applicant Privacy Notice Personal Information We Collect Your Privacy Choices Team Overview The Analytics Enablement team is a strategic product pillar within Trans Union’s Credit Risk Solutions organization. The team helps customers derive greater value from Trans Union data through insights, analytics, modeling, decisioning, and AI-enabled solutions.

The Principal, AI Product Management, TruIQ will define and drive the AI enablement strategy across the TruIQ portfolio. This hands-on, Director level individual contributor will bring deep AI product expertise to the team, influence without direct authority, and help Product, Engineering, and other partners responsibly integrate AI and agentic capabilities into existing and future solutions. This role reports to the VP, Product Management – Analytics & Insights and partners horizontally with Product Managers across the TruIQ portfolio.

This is a hybrid position and involves regular performance of job responsibilities virtually as well as in-person at an assigned TU office location for a minimum of two days a week.

Role Overview and Core Responsibilities Define the AI enablement vision, strategic direction, multi-year roadmap, and commercialization plan for TruIQ Analytics Solutions.

Identify where existing products should be enhanced, redesigned, or rebuilt to effectively support AI, intelligent assistants, agentic workflows, and automation.

Conduct voice-of-customer research and translate customer needs into clear product requirements, prioritized roadmaps, market positioning, and measurable outcomes.

Write clear product requirements documents that define customer problems, proposed solutions, scope, requirements, tradeoffs, risks, governance needs, and success measures.

Partner with Engineering, Data Science, Architecture, Design, Security, Compliance, Data Governance, Operations, and go-to-market teams from discovery and prototyping through launch and ongoing optimization.

Provide informed product direction to Engineering by defining effective requirements, asking the appropriate technical questions, evaluating proposed approaches, and balancing technical feasibility with customer and commercial value.

Establish product goals and evaluation frameworks spanning adoption, customer value, productivity, cost efficiency, pricing, revenue potential, and responsible AI performance.

Test and validate AI-enabled capabilities while incorporating feedback loops, human oversight, guardrails, monitoring, and internal governance requirements.

Build customer confidence in AI-enabled capabilities, particularly within highly regulated and risk-sensitive environments.

Mentor and upskill Product Managers and other cross-functional partners on AI product practices, emerging technologies, customer use cases, and responsible adoption.

Required Knowledge and Experiences
10+ years of experience in product management, product strategy, analytics solutions, or a related discipline, including established product-management experience defining product vision, roadmaps, requirements, and commercialization strategies.

Demonstrated experience developing, launching, integrating, or scaling AI-enabled, agentic, analytics, data, SaaS, or enterprise technology products that produced measurable customer and business outcomes.

Strong customer orientation, with experience using voice-of-customer research, user insights, experimentation, market intelligence, and competitive analysis to translate customer needs into executable product strategies.

Experience influencing technical and business partners without direct authority, including the ability to guide and appropriately challenge Engineering while building alignment across diverse stakeholders.

Experience introducing technology in a highly regulated or governed environment and balancing innovation with security, compliance, customer trust, risk tolerance, and responsible adoption.

Required Technical Skills Strong understanding of Generative AI, Agentic AI, ML (Machine Learning), LLMs (Large Language Models), RAG (Retrieval-Augmented Generation), workflow automation, AI evaluation, responsible AI, and enterprise software product development.

Experience defining requirements for AI-enabled products, including feedback loops, human-in-the-loop controls, guardrails, monitoring, evaluation approaches, and continuous improvement.

Ability to partner with technical teams on scalable integration and interoperability approaches, including APIs (Application…