AI / ML Engineer II / III

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.

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.

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

deep learning and simulation problems, particularly with large datasets.

Responsibilities

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

Requirements

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 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.