AI Product Ops & AI Enablement Lead

NIQ is standing up a new PM Operations & AI Enablement group: a small, build-weighted internal product team that creates AI-powered tools for our product organization and drives the adoption that makes them land. We are looking for the Lead Product Manager who will own that portfolio end to end.

This is a product management role, not a coordination role. You will carry a portfolio of internal products with real users, a measured quality bar, adoption targets and sunset decisions — and you will own the operating practices that make our product teams faster and better evidenced. Think of it as being the product manager for the product managers.

You will lead a cross-functional group of engineers without direct reporting lines, which means you will deliver through shared goals, written agreements and visible artifacts rather than instruction. We are explicit about this because it shapes who succeeds here.

The two pilots are an AI toolchain for the software development lifecycle, and our design system.

Strategy and portfolio

– The group’s product vision, direction and strategy for a 12–18 month roadmap: what gets built, in what order, and why.

– Portfolio prioritization and capacity allocation, including how many tools the group can responsibly carry at once.

– The build-versus-buy recommendation for every initiative, against a buy-or-configure-first default, and the business case behind it.

– The sunset decision for tools that do not earn their adoption.

– A two-to-three year view of how AI changes product and engineering work, and a roadmap that stays consistent with it.

Building AI products

– End-to-end product definition for the group’s AI tooling: problem, users, the workflow it replaces, the adoption path, the measurement plan, the maintenance owner.

– AI-native specifications a strong engineer can build from — task boundaries, context sources, failure modes, guardrails, human-in-the-loop points, and the quality bar in numbers.

– The product calls that shape the architecture: workflow versus agent, retrieval versus fine-tuning, model selection per use case, and the cost and latency budget.

– The quality bar and evaluation strategy: what "good enough" means before the build starts, a failure taxonomy built from real usage, and the regression discipline when prompts or models change.

– How the human enters the loop, how uncertainty is shown, and what happens when the system does not know — the decisions that determine whether people trust the tool.

– The incident and rollback plan for non-deterministic failure, written before launch.

Adoption

– The adoption outcome, measured as instrumented depth of use — not seats provisioned or enthusiasm in a demo.

– The adoption path for each tool: pilot teams, a champion in each team, onboarding, office hours, handover to support.

– Evangelizing the work across product and engineering: the demo, the prototype, the case made repeatedly and well.

– Facilitating the sessions where practice actually changes and leaving them with commitments rather than sentiment.

– Change management, rollout, training and enablement content.

– Honest reconciliation of instrumented usage against self-reported benefit, and ownership of the gap.

Practice and craft

– The product operating standards for the organization — intake, prioritization inputs, PRD conventions, definition of done, documentation, decision log — kept deliberately light.

– Coaching and mentoring product managers, particularly those earlier in their careers, in continuous discovery, outcome framing, evidence-based decision-making and AI-native practice.

– The AI literacy curriculum for the product organization — designed and taught or outsourced.

– Recurring forums where teams share what they discovered and what they decided.

Measurement and reporting

– The definition, baseline and instrumentation for the programme’s success metrics, quantitative and qualitative.

– A metric-led reporting cadence to senior leadership, including the results that did not work.

– The trade-off framework — speed, reliability, cost, data risk — communicated in writing.

Governance partnership

– The product-side data decisions: what data each surface accepts, which model serves which use case, and the guardrails that go with it.

– Partnership with Legal, Privacy, Security and IT on data classification, model approval, access and responsible-AI standards.

Required

– Senior product leadership experience in enterprise B2B SaaS, with end-to-end ownership from discovery through delivery, and the ability to show what changed as a result.

– Experience leading and growing cross-functional teams, and mentoring product and design practitioners.

– Demonstrated ability to deliver through people who do not report to you — with concrete examples of aligning stakeholders who had different priorities, and of moving an organization to a new way of working that stuck.

– Hands-on experience designing and shipping generative or agentic AI capabilities in a live product — not only using AI tools to accelerate your own work.

– Working fluency in AI product practice: prompt and context design, retrieval, agentic workflow patterns, guardrails, human-in-the-loop design, and model evaluation. You can discuss model trade-offs, data requirements, cost and latency with engineers without needing first-principles explanations.

– Experience defining what "good enough" means for an AI feature in measurable terms, and holding a product to it. – Experience creating or scaling a design system and driving its adoption across teams.

– Strong research and evidence practice: usability testing, interviews, journey mapping, surveys, A/B testing — run to a professional standard and taught to others.

– Strong customer research and evidence practice: conducting interviews and gathering insights for creating informed decisions.

– Product analytics fluency (for example Amplitude, Pendo, Tableau) and the quantitative literacy to define a metric that survives scrutiny.

– Experience defining and embedding operating practice — design ops, product ops, intake, process and documentation standards — that teams adopted willingly.

– Facilitation skills with senior and skeptical audiences, and experience designing and delivering enablement or training programmes.

– Commercial judgement: business-case creation, budget ownership, and vendor or partner evaluation.

– Excellent written communication: you make your case in a document that survives the meeting you were not in.

– Fluent English

Preferred

– Formal grounding in AI product development

– Experience with agentic development tooling in your own workflow, and a view on what it changes about how teams work.

– Familiarity with engineering productivity measurement (DORA, SPACE, DX Core 4) and clear views on what each does and does not measure.

– Experience with eval and observability tooling for LLM applications.

– Working knowledge of AI governance obligations in the EU and beyond, including AI literacy requirements for internal AI use.

– Experience of release and platform operations, CI/CD or environment management.

– Prior experience standing up a new function, or a founder background.

Our Benefits

• Flexible working environment
• Volunteer time off
• LinkedIn Learning
• Employee-Assistance-Program (EAP)

NIQ may utilize artificial intelligence (AI) tools at various stages of the recruitment process, including résumé screening, candidate assessments, interview scheduling, job matching, communication support, and certain administrative tasks that help streamline workflows. These tools are intended to improve efficiency and support fair and consistent evaluation based on job-related criteria. All use of AI is governed by NIQ’s principles of fairness, transparency, human oversight, and inclusion. Final hiring decisions are made exclusively by humans. NIQ regularly reviews its AI tools to help mitigate bias and ensure compliance with applicable laws and regulations. If you have questions, require accommodations, or wish to request human review were permitted by law, please contact your local HR representative. For more information, please visit NIQ’s AI Safety Policies and Guiding Principles: https://nielseniq.com/global/en/info/niqs-ai-safety-policies/

About NIQ

NIQ is the world’s leading consumer intelligence company, delivering the most complete understanding of consumer buying behavior and revealing new pathways to growth. In 2023, NIQ combined with GfK, bringing together the two industry leaders with unparalleled global reach. With a holistic retail read and the most comprehensive consumer insights—delivered with advanced analytics through state-of-the-art platforms—NIQ delivers the Full View™. NIQ is an Advent International portfolio company with operations in 100+ markets, covering more than 90% of the world’s population.

For more information, visit NIQ.com

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Our commitment to Diversity, Equity, and Inclusion

At NIQ, we are steadfast in our commitment to fostering an inclusive workplace that mirrors the rich diversity of the communities and markets we serve. We believe that embracing a wide range of perspectives drives innovation and excellence.  All employment decisions at NIQ are made without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, marital status, veteran status, or any other characteristic protected by applicable laws. We invite individuals who share our dedication to inclusivity and equity to join us in making a meaningful impact. To learn more about our ongoing efforts in diversity and inclusion, please visit the https://nielseniq.com/global/en/news-center/diversity-inclusion