Nielsen measures the media-consumption habits of homes globally. Our data paints a rich portrait of the audience in those markets we measure. As a pioneer and industry leader in audience measurement for more than half a century, we take pride in our technology and processes, and we are constantly searching for new ways to advance our culture of innovation and growth. As a Lead Data Scientist you will be on the front lines of the maintenance and ongoing evolution of the data science behind the Audio audience measurement business. You will join a team focused on delivering and enhancing Nielsen’s product. The team develops new techniques and processes data to better meet the needs of our clients to measure audiences, working at the intersection of Data Science, Technology, Product Delivery and Operations. Despite being spread geographically, our team values collaboration, teamwork, and having fun at work! We hold each other accountable for creating stable, scalable, and well-documented solutions.
Responsibilities
Research, design, develop, implement and test econometric, statistical, optimization and machine learning models.
Design, write and test modules for Nielsen analytics platforms using Python, R, SQL and/or Spark.
Utilize advanced computational/statistics libraries including Spark MLlib, Scikit-learn, SciPy, StatsModels or R.
Collaborate with cross functional Data Science, Product, and Technology teams to integrate best practices from across the organization
Provide leadership and guidance for the team in the of adoption of new tools and technologies to improve our core capabilities
Execute and refine the roadmap to upgrade the modeling/forecasting/control functions of the team to improve upon the core service KPI’s
Gain a deep understanding of the Audio methodology supported by the team
Ensure product quality, stability, and scalability by facilitating code reviews and driving best practices like modular code, unit tests, and incorporating CI/CD workflows
Explain complex data science (e.g. model-related) concepts in simple terms to non-technical internal and external audiences
Key Skills
5+ years of professional work experience in Statistics, Data Science, and/or related disciplines, with focus on delivering analytics software solutions in a production environment
Expertise with relational and distributed database systems with expertise in tools such as Spark, Presto, and other SQL-based querying engines
Expertise in coding and testing of analytical modules using Python, SQL and Spark.
Expertise in at least one statistical software or machine learning package, such as R or Scikit-learn
Expertise with optimization techniques and tools such as AIMMS, Gurobi, etc
Expertise with DevOps tools and CI/CD workflows including Git
Expertise with sophisticated data mining forecasting & modeling solutions and the tools that support them such as Scikit-learn, PyTorch, and PyMC3
Well-organized, clear communication, and an ability to handle multiple competing priorities in a fast-paced environment
Graduate/Post Graduate degree in Statistics, Economics, Applied Mathematics, Computer Science, Engineering or other Quantitative field of study
Exceptional problem solving skills. Abilities to solve problems independently and within a cross-functional team to resolve any hurdles in the projects
Critical thinking skills to evaluate results in order to make timely decisions
Mentor and provide guidance to other data scientists
(Preferable) Experience working in cloud-based environments such as Azure, AWS, or GCP
(Preferable) Experience with ETL frameworks like Airflow
(Preferable) Domain expertise in survey sampling and/or audience measurement
