Nielsen is hiring a
Junior Data Scientist in United States

Summary

The job is for a Data Analyst at Nielsen focusing on audience measurement and streaming meter technology. The role involves learning about Nielsen's TV panel data, designing and executing analyses, developing data pipelines, and providing data-driven recommendations. The position requires a Bachelor's Degree or higher in relevant fields, experience with Python, PySpark, SQL, Tableau, AWS, and strong communication skills.

Requirements

• Bachelor’s Degree or higher in Mathematics, Statistics, Computer Science, Data Science, Data Engineering or equivalent experience
• Proven years of experience with the following: Manipulating, analyzing and interpreting complex data sources to tell a story from data through analyses; Programming in Python, PySpark and SQL for data pipeline development used to support analytics; Trending and visualizing data within Tableau; Experience working in cloud-based environments such as AWS
• Able to communicate effectively both orally and in writing
• Embraces innovation with a readiness to explore new solutions and a commitment to rapid iteration based on customer feedback
• Demonstrates strategic thinking, understanding the broader impact of data insights on product and business objectives
• Good level of English

Responsibilities

• Learn and become an expert in Audience Measurement, with a focus on streaming meter technology used for data collection and crediting
• Contribute to analysis plan design, develop underlying data pipelines, execute analyses and summarize results
• Identify, clean, and summarize data from disparate and complex sources to facilitate high-quality analyses
• Create simple, meaningful visualizations to support data analysis and stakeholder communications
• Develop and share efficient code used for analyses
• Incorporate quality checks to proactively detect and correct errors throughout analysis
• Provide clear data-driven recommendations based on research findings
• Support senior team members in collaboration with department stakeholders (Engineering, Data Science, Technology, etc.)