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.
- Collaboration & Mentorship: Collaborate with cross-functional teams (e.g., engineers, product managers, domain experts) to define research questions and implement data-driven solutions.
- Learn and become an expert in US TV Audience Measurement, with a focus on data structures, computations, and sampling/weighting.
- Data Quality : Define and implement data quality assessments. Perform creative and effective analyses of input and output data.Create dashboards with informative data visualization
Qualifications
- Education: Master's or Ph.D. in Statistics, Biostatistics, Data Science, or a closely related quantitative field.
Technical & Statistical Software:
- Proficiency in Python, Spark, SQL, Artificial intelligence and Machine learning
- Working knowledge of bash and git.
- Experience working with cloud-based or big data technologies (e.g. Databricks, Airflow, AWS)
- Ability to work alone and in a team
- Strong communication and presentation skills, with a keen eye for detail;
- Experience with taking the (technical) lead in projects; project management experience and working in an agile setting is a plus
Expertise:
- Deep theoretical and practical understanding of probability, statistical inference, hypothesis testing (especially ANOVA), and experimental design.
- Demonstrated experience with various sampling techniques and their associated statistical challenges (e.g., weighting, variance estimation).
- Proven ability to develop and deploy machine learning models in a real-world setting.
- Strong analytical, problem-solving, and critical-thinking skills. Excellent written and verbal communication.
Additional Information
- Experience with A/B testing design and analysis.
- Machine learning model development
- Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch).
- Experience using cloud computing platforms (e.g., AWS, Azure, GCP) for data processing and model deployment.
- Knowledge of Bayesian statistics or causal inference methods.
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