Senior AI / ML Data Scientist II

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 at this level, you will be a key contributor to our team, responsible for building and deploying end-to-end machine learning models. You will work with a degree of autonomy on well-defined projects, translating business needs into functional and scalable ML solutions. This role is perfect for hands-on ML Engineers looking to deepen their expertise and take on more complex challenges.

Responsibilities

serving.

● Contribute to technical design discussions and provide input on system architecture and

best practices.

● Collaborate with business, product, and other engineering teams to understand

requirements and translate them into technical specifications.

● Write high-quality, production-ready code and participate in code reviews to maintain our

standards of excellence.

● Mentor interns or junior engineers, sharing your knowledge and expertise.

Responsibilities

Independently build, train, and deploy machine learning models for complex projects.; Design and maintain robust, end-to-end ML pipelines, from data processing to model

Requirements

Bachelor's or Master's degree in Engineering, Mathematics, Statistics, or a related field.; 3-6 years of professional experience in machine learning engineering.; Proven experience in building and deploying ML models in a production environment.; Strong proficiency in Python, SQL, and experience with a distributed computing framework (e.g., Spark).; Data Analytics Experience: Strong background in data analytics, including statistical analysis, data visualization, and reporting to derive business insights.; AI Experience: Practical, hands-on experience with modern AI frameworks and techniques, including LLMs or Generative AI applications.; Familiarity with workflow orchestration tools (e.g., Airflow) and ML platforms (e.g., MLflow) is preferred.; Knowledge of classification models, regression models, anomaly detection, boosted models, deep learning, and simulation problems, particularly with large datasets.; Strong problem-solving skills and the ability to work independently on projects.