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 Sr. 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 professional by leading complex data analyses and building out scalable analytic capabilities across the team, while also developing subject-matter expertise in digital methodology and cross-platform audience measurement. 
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 work closely with data scientists, data engineers, product managers, and others that are committed  to building out and bringing to market new methodologies and data products that accurately estimate audience duplication across multiple screens (e.g. TV, PC, mobile, OTT). 
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. 

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 products
• Lead comprehensive analyses to evaluate cross-platform audience outcomes for internal R&D use cases, as well as those resulting from client inquiries
• Build out analytic capabilities for the broader team, including the creation of scalable tools and best practices
• Stay 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 measurement
• Stay informed of new research and developments in the field and participate in internal and external knowledge exchanges (conferences, workshops, webinars)  

Is this for me?

• The ideal candidate will have:
• Extensive experience applying statistical models and/or machine learning on large, complex data sets
• Extensive data analysis experience, including EDA, data wrangling, and visualization
• Proficiency in at least 2 of: Python, SQL, Spark
• Experience working with big data in cloud environments (e.g. AWS, Databricks)
• Led complex projects with multiple stakeholders, including interfacing directly with external clients
• Strong communication & presentation skills (written and verbal)
• Graduate degree in computer science, software engineering, information systems, data science, or other hard science (e.g. statistics, physics, etc.) and 2+ years of work experience OR undergraduate degree and 3+ years of work experience with outstanding analytical expertise and strong technical leadership 

Preferred Skills:

• Industry knowledge of digital audience / media measurement
• Experience with core Data Science tech stack (pandas, scikit learn,etc)
• Experience with source control using git and CI/CD workflows
• Experience with container technologies such as Airflow, Docker, and/or Kubernetes