Statistician I

Company Description

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 Media is the global leader in measuring what people watch and listen to on TV, Digital and Audio in a changing media landscape. Data Science is essential to Nielsen’s mission. We offer a fast-paced environment for methodology and software development in a team of world-class computer scientists, statisticians, data scientists, and behavioral methodologists

This job description outlines the responsibilities and qualifications for a Data Scientist role with a primary focus on Statistics, Research methodologies, Sampling, ANOVA, and the application of these principles within Machine Learning and Data Science contexts.

Key Responsibilities

• Statistical Research & Analysis: Design, execute, and interpret ANOVA (t-tests, multi-factor, repeated measures) and other parametric/non-parametric tests to test hypotheses, draw statistically sound conclusions, and inform business or scientific decisions.

• Sampling Strategy & Design: Develop and implement effective sampling methodologies (e.g., random, stratified, cluster, systematic) to ensure data representativeness, minimize bias, and optimize resource allocation for data collection efforts. Calculate and justify appropriate sample sizes and power analysis.

• Data Preparation & Feature Engineering: Clean, preprocess, and transform complex datasets, applying statistical principles to feature selection and engineering to enhance model performance.

• Reporting & Communication: Communicate complex analytical results, statistical methods, and model performance metrics clearly and concisely to technical and non-technical stakeholders through reports, visualizations, and presentations.