Senior Product Manager II - Research/Survey methodologie

Company Description

At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content - wherever and whenever it’s consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future.

Job Description

- Define, communicate, and drive the refinement of the Product Roadmap, aligning strategy with overall business goals.

- Oversee the end-to-end product life cycle, from concept and development to launch and iteration.

- Prioritize the product roadmap, including a clear understanding of requirements, resources, execution timelines, and impact.

- Drive a consistently groomed product backlog in partnership with Engineering, Data Science, and Operations teams.

- Identify market trends, customer needs, and competitive landscape to inform product strategy.

- Lead stakeholder communication by consistently communicating priority decisions, associated trade-offs, and clear visions to management and other stakeholders.

- Drive cross-functional collaboration across engineering, data science, sales, and marketing to orchestrate the delivery of high-impact product solutions.

- Author requirements and features based on the voice of the customer, independently translating product strategy into execution by leading client feedback and Voice of Customer (VOC) meetings.

- Identify customer problems and workflow opportunities where AI, machine learning, and emerging technologies can unlock product capabilities and measurable business value.

- Partner closely with engineering, data science, and architecture teams to implement AI-powered solutions using appropriate approaches and tools, including machine learning, model orchestration, and MCP.

- Establish clear product requirements, success metrics, guardrails, and evaluation criteria for AI functionality, including quality, reliability, explainability, performance, and user adoption.