AI / ML Data Scientist II

About the Role
This job description outlines the responsibilities and qualifications for an AI / ML Data Scientist II role with a primary focus on designing, building, and deploying scalable machine learning systems, software engineering best practices, and the application of AI/ML principles within Machine Learning and Data Science contexts.
Audience is everything! Nielsen offers the world's most complete measurement of media-viewing behavior. Our data scientists and ML engineers leverage the best data assets and modeling methods to build consistent and comparable metrics of the media landscape in multiple countries. We help the world's biggest brands make better and faster media and marketing decisions.
Responsibilities
Implement, test, and optimize econometric, statistical, and Machine Learning (ML) models to enhance data accuracy and reporting.
Identified the AI opportunities in existing and new projects and implemented them.
Quickly learn and become an expert in Audience Measurement and the associated digital meter technology for data collection.
Design comprehensive ML workflows, develop reliable data pipelines (using Databricks/AWS S3), execute sophisticated model training and evaluation, and clearly summarize results.
Identify, integrate, clean, and transform data from disparate and complex data sources to ensure high-quality inputs for ML models.
Develop, share, and maintain efficient, well-documented, production-quality code following software engineering best practices.
Proactively incorporate quality checks throughout the ML development lifecycle to detect and correct errors swiftly.
Create simple, meaningful visualizations (e.g., using Tableau) to effectively support data analysis and clearly communicate insights and findings to stakeholders.
Translate complex research findings into clear, data-driven recommendations that address business needs and drive product/technology improvements.
Partner closely with department stakeholders (Engineering, Data Science, Product, Technology) to manage timelines, share data, execute tests, and present results.

Employment type
Full-time

Work arrangement
No