AI / ML Engineer II / III

About the Role: As a Machine Learning Engineer, you will take a leading role in designing and building the end-to-end systems that power intelligent products at Nielsen. You will own complex projects from conception to deployment, making critical design and architectural decisions. This is a high-impact role for a hands-on engineer who can handle ambiguity, mentor others, and deliver robust, scalable ML solutions. Responsibilities: • Own the complete lifecycle of machine learning systems, including problem formulation, data pipeline design, model development, deployment, and monitoring. • Make key architectural and design decisions to ensure our ML systems are scalable, reliable, and efficient. • Navigate project requirements, creating clear plans of action and defining technical roadmaps. • Optimize complex data pipelines and ML models for performance. • Mentor junior engineers on the team, fostering their growth and ensuring high technical standards. • Act as the technical point of contact for your projects, managing communication with product managers and other stakeholders. • Cultivate a team environment focused on continuous learning, where innovative audience measurement methodologies are developed and refined through collaborative effort

Basic Qualifications: • Master’s or Bachelor’s degree in Engineering, Mathematics, Statistics or a related field. • A proven track record of owning and shipping production machine learning systems. • Deep expertise in classification models, regression models, anomaly detection, boosted models, deep learning and simulation problems, particularly with large datasets. • Deep expertise in ML concepts, data engineering, and MLOps practices. • Proficiency in Python and SQL. • Must have deployed E2E ML systems by automating training and inference pipelines. • Hands-on experience with distributed computing frameworks like Apache Spark. • Experience with workflow orchestration tools (e.g., Airflow) and MLOps.

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