Senior Data Scientist I

Overview

At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content - wherever and whenever it’s consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future.

Job Description

• 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.
• Our areas of expertise include researching hardware, software, quality controls and business rule changes that will improve the accuracy of our data reporting.
• In this role, you will learn and become an expert in Nielsen’s Panel data, with a focus on Meter hardware and software used for data collection and crediting. You will design analysis plans, develop underlying data pipelines, execute analyses and summarize results. You will collaborate with stakeholders across Data Science, Engineering, Product and Technology teams to provide clear data-driven recommendations based on research findings.
  
  

• Implement, test and optimize econometric, and statistical models.
• Learn and become an expert in Audience Measurement, with a focus on digital meter technology used for data collection and crediting.
• Design analysis plans, develop underlying data pipelines, execute analyses and summarize results.
• Identify, clean, and summarize data, potentially from disparate and complex data sources, to facilitate high quality analyses.
• Create simple, meaningful visualizations to help support data analysis and stakeholder communications
• Develop and share efficient code used for analyses.
• Incorporate quality checks to proactively detect and correct for errors throughout analysis.
• Provide clear data-driven recommendations based on research findings.
• Collaborate with department stakeholders (Engineering, Data Science, Technology, etc.). This includes providing status updates, developing timelines, sharing data, executing tests, presenting results, etc to ensure business needs are met.
  
  

• Bachelor’s Degree or higher in Mathematics, Statistics, Computer Science, Data Science, Data Engineering or a related field.
• 5-10 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
• Big Data & Cloud: Extensive experience with PySpark and building data pipelines in cloud-based environments, specifically AWS (including services like S3 and proficiency with Databricks)
• Project Management: Excellent ability to manage and prioritize multiple, concurrent assignments with strong attention to detail.
• 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
• Strategic & Innovative Mindset: Demonstrates strategic thinking, understands the broader business impact of data insights, embraces innovation, and is committed to rapid iteration based on feedback.
  

• Advanced Analytics: Deep understanding and application of Statistics, including hypothesis testing, regression analysis, and experimental design.
• Machine Learning/AI: Practical experience implementing and applying Machine Learning (ML) and understanding Artificial Intelligence (AI) concepts.
  
  

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