Sr. Data Scientist

Data Science is at the core of Nielsen’s business. Our team of researchers come from diverse disciplines and they drive innovation, new product ideation, experimental design and testing, complex analysis and delivery of data insights around the world. We support all International Media clients and are located where our clients are.
What is the role?Data Science is core to what Nielsen does, and our research projects have high visibility in directly affecting the results of our business and our clients. This Data Scientist role in the Nielsen ONE Digital and Deduplication team provides an opportunity to contribute to methodological innovation and pipeline development in the exciting and fast-changing world of media measurement. This is an ideal position to grow as a data scientist and/or data engineer, implement scalable solutions, and develop subject-matter expertise in digital methodology and cross-platform audience measurement. 
What will I do?Design and implement robust Data Science workflows that solve for cross-platform audience estimation through cutting-edge methodologies Develop processes to maintain and enhance these methodologies throughout their lifecycle in client-facing productsDevelop analyses to evaluate cross-platform audience outcomes resulting from methodologiesProvide support for the identification and implementation of best practices to improve performance and scalability of data modeling approachesStay up-to-date on industry changes to digital measurement (e.g. new devices and platforms, privacy law updates, changes in browser/app measurement, etc.) and critically assess how it would impact Nielsen measurementStay informed of new research and developments in the field, as well as participate in internal and external knowledge exchanges (conferences, workshops, webinars)  
Who am I working with?The Nielsen ONE Digital and Deduplication team within the Data Science Global Media organization focuses on improving and enhancing Nielsen’s highly successful products in the marketplace for digital & TV advertising and content measurement. As part of this exciting team, this position will lead the development of production pipelines to implement new methodologies to accurately estimate audience duplication across multiple screens (e.g. TV, PC, mobile, OTT), enhancing Nielsen's cross-media measurement capabilities. 
Why do I want to work here?As the arbiter of truth, Nielsen Global Media fuels the media industry with unbiased, reliable data about what people watch and listen to. To discover what’s true, we measure across all channels and platforms⁠—from podcasts to streaming TV to social media. And when companies and advertisers are armed with the truth, they have a deeper understanding of their audiences and can accelerate growth.   

Is this for me?

• The candidate will have specific experience supporting data science pipelines, including productionalizing machine learning and/or statistical models.
• The candidate will have demonstrated data analysis experience including data wrangling and visualization.
• Graduate degree in computer science, software engineering, information systems, data science, or other hard science (e.g. statistics, physics, etc.)
• 1+ year of work experience OR undergraduate degree and 3+ years of work experience with outstanding analytical expertise and strong technical leadership
• Proficiency in Python, Spark, and SQL
• Experience working with big data in cloud environments (e.g. AWS, Databricks)
• Experience with source control using git and CI/CD workflows
• Leading and managing complex projects with multiple stakeholders
• Strong communication & presentation skills (written and verbal) 

Preferred Skills

• Industry knowledge of digital audience / media measurement
• Experience working independently and as part of cross-functional teams
• Experience with core Data Science tech stack (pandas, scikit learn)
• Experience wrangling, analyzing, and correcting very large datasets using statistical models and/or machine learning
• Experience with container technologies such as Airflow, Docker, and/or Kubernetes