At Nielsen, our goal is to build a better media future for all people. Our focus is on representing all audiences and understanding how they interact with content and advertising. As part of Nielsen’s Campaign Analytics team you will be part of a data science team focused on building scalable solutions to answer the questions like: What was the return on investment for specific media spend? Which marketing type drove the highest return on investment? How do we measure the causal relationship between advertising and engagement/sales? How can we use data to help our clients plan for the future? Our Campaign Analytics Data Science associates come from diverse disciplines that include business, economics, engineering, mathematics, operations research, physics and statistics. These professionals drive innovation and insight through continual ideation, complex analyses and delivery of insights to our clients. Because measurement is at the core of Nielsen’s business, our products have high visibility and make a direct impact on our clients.
Responsibilities
Use and fit different mathematical and econometric models for explanatory purposes that deliver better decisions, and create high-quality data visualizations for internal and external purposes.
Research, design and implement analytic and mathematical approaches and algorithms to build scalable best of class web-based analytical solutions
Design and test analytical modules for Nielsen modeling platforms
Partner with our Software Engineering department to build best-of-class web-based analytical solutions
Document and present methodology inside and outside the company
Design and prioritize work for smaller teams
Mentor newly hired associates
Requirements
PhD or Masters degree in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or Related field
6+ yrs experience in working in Data Science and/or Statistical analysis
Expertise in Python, Spark, SQL or other modern programming languages
Experience in using machine learning libraries and techniques
Proficient in writing production grade code using scientific computing packages (e.g., NumPy, SciPy, Scikit-learn)
Experience with Bayesian and Frequentist statistics
Experience with cloud providers (e.g. AWS, Azure)
Experience in code management, docker, and CI/CD pipelines
Experience with Num Pyro is a plus
Well-organized and capable of leading multiple mission-critical projects simultaneously while meeting deadlines
