Product Manager – AI, Consumer Panel Data Platform

Short Summary

Product Manager – AI, Consumer Panel Data Platform with NielsenIQ. Focus Areas Your work will center on three strategic pillars: 1. AI-Powered Data Validation & Quality – Define and deliver AI/ML capabilities that validate panel data at scale — including anomaly and outlier detection, automated root-cause analysis of data quality issues, and intelligent quality assurance across the end-to-end data pipeline. You will move…

Key Details

• Position / opportunity: Product Manager – AI, Consumer Panel Data Platform
• Organization: NielsenIQ
• Country / coverage: Colombia
• Location: Bogota, DC, Colombia
• Work arrangement: On-site
• Opportunity type: Volunteer opportunities
• Sector: Education
• Compensation: Not specified
• Duration: Not specified
• Start date: 2026-08-20
• Application deadline: Not specified

Description

Focus Areas Your work will center on three strategic pillars: 1. AI-Powered Data Validation & Quality – Define and deliver AI/ML capabilities that validate panel data at scale — including anomaly and outlier detection, automated root-cause analysis of data quality issues, and intelligent quality assurance across the end-to-end data pipeline. You will move validation from manual, reactive review toward proactive, automated, and explainable quality systems. 2. ML & AI Models for Product Coding & Decoding – Own the product direction for the ML and AI models behind our product coding and decoding services — the classification, item-description interpretation, and barcode/product assignment capabilities that are foundational to the accuracy of our panel data output. You will partner deeply with data science to improve match rates, coverage, and precision, and to standardize decoding approaches across markets. 3. AI Automation of the Platform – Identify and prioritize opportunities to automate platform workflows using AI — including agentic and LLM-based approaches — to reduce manual operational effort, accelerate cycle times, and enable the platform to scale. You will evaluate emerging AI techniques and translate them into production-grade automation with clear business value. What You'll Do Drive the AI Validation and Automation product roadmap for the Consumer Panel input data platform, aligning data science, engineering, operations, and product leadership around a clear strategy and priorities. Translate ambiguous business problems into well-defined AI/ML product requirements, success metrics, and phased delivery plans. Lead cross-functional initiatives end to end — from opportunity sizing and model feasibility through pilot, rollout, and adoption. Define how AI model performance is measured in business terms (accuracy, match rate, coverage, cost per transaction, time-to-data) and hold solutions accountable to those outcomes. Partner with data science on model development priorities, evaluation frameworks, and the tradeoffs between automation and human-in-the-loop review. Drive standardization of coding/decoding approaches across markets and data sources. Communicate strategy, progress, and results to senior stakeholders across Product, Technology, Operations, and Commercial teams. Stay ahead of AI/ML and agentic-automation trends and pressure-test their applicability to real production data workflows. What You Bring 3+ years of product management experience (or equivalent) with demonstrated ownership of technical or data-intensive products; experience shipping AI/ML-powered capabilities strongly preferred. Working fluency in AI/ML concepts — model training and evaluation, classification, LLMs, and automation patterns — and the judgment to know where AI adds value versus where it doesn't. Experience with large-scale data pipelines, data quality, or data operations environments. Strong analytical skills: comfortable defining metrics, interrogating model performance, and making evidence-based prioritization decisions. Proven ability to lead cross-functional teams and influence senior stakeholders without direct authority. Excellent written and verbal communication skills in English; able to make complex technical topics clear to non-technical audiences. Curiosity, adaptability, and a bias for action in a fast-evolving domain. Why This Role Matters The quality of NielsenIQ's Consumer Panel output depends on how well we validate, code, and process raw consumer data — and AI is redefining what's possible in each of those steps. You'll own the capabilities that determine data quality, speed, and scale for one of the industry's most important consumer data assets, with direct impact on new products and the company's AI strategy. 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…

Responsibilities

• Focus Areas Your work will center on three strategic pillars: 1.
• AI-Powered Data Validation & Quality – Define and deliver AI/ML capabilities that validate panel data at scale — including anomaly and outlier detection, automated root-cause analysis of data quality issues, and intelligent quality assurance across the…
• You will move validation from manual, reactive review toward proactive, automated, and explainable quality systems.
• ML & AI Models for Product Coding & Decoding – Own the product direction for the ML and AI models behind our product coding and decoding services — the classification, item-description interpretation, and barcode/product assignment capabilities that are…
• You will partner deeply with data science to improve match rates, coverage, and precision, and to standardize decoding approaches across markets.
• AI Automation of the Platform – Identify and prioritize opportunities to automate platform workflows using AI — including agentic and LLM-based approaches — to reduce manual operational effort, accelerate cycle times, and enable the platform to scale.

Requirements / Eligibility

• , success metrics, and phased delivery plans.
• Lead cross-functional initiatives end to end — from opportunity sizing and model feasibility through pilot, rollout, and adoption.
• Define how AI model performance is measured in business terms (accuracy, match rate, coverage, cost per transaction, time-to-data) and hold solutions accountable to those outcomes.
• Partner with data science on model development priorities, evaluation frameworks, and the tradeoffs between automation and human-in-the-loop review.
• Drive standardization of coding/decoding approaches across markets and data sources.

How To Apply

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