Product Analyst at NielsenIQ

We are looking for a meticulous and curious Product Analyst to join our Data Governance team, with a focus on providing robust analysis of our data source quality, defining metrics and reporting to generate actionable insights that drive decision making and trust in the data that powers our products and services. As we expand our data sources, we need to understand their relative quality and depth to help us prioritise and harmonise product data in a way that provides the most insightful benefit for our clients. This role will work cross-functionally across our product teams and with key internal stakeholders, providing regular updates as well as targeted support and insight as required. The Label Insight Data Governance Team, situated in our Product Team, is dedicated to ensuring the quality, integrity, and usability of data across the organization. Comprising three specialized teams—Attribute Health, Source Quality, and Data Support—it works collaboratively to uphold robust data standards, validate AI-generated outputs, and provide responsive, client-focused support. Together, the team enables scalable, trustworthy, and insight-ready data that powers our products and services. Key Responsibilities: · Evaluate the relative and overall quality of our data sources including data produced by AI models, web scraping, manual coding, and other coded sources · Define and implement metrics that provide actionable insights to both improve data reliability and quality, and help support prioritisation and harmonisation of our data sources · Provide consistent reporting on data source quality – using dashboards and reports to communicate trends and issues, and proposed actions to address · Identify anomalies, patterns, and potential risks in these data sources, with a key focus on ensuring the confidence of AI-generated data · Design and implement methods to detect outliers, inconsistencies, and unexpected trends in data sources · Investigate root causes of anomalies and collaborate with technical teams and key stakeholders to address underlying issues · Support the application of validation rules, checks, and metrics to monitor data quality over time · Collaborate with data scientists and engineers to refine AI outputs based on quality findings · As needed, support taxonomy recognition tasks to ensure our attributes achieve the required quality thresholds