Data Scientist II

Nielsen

Data Scientist at Nielsen developing models and conducting complex data analyses for audience measurement. Collaborating with stakeholders and delivering data-driven solutions in a fast-paced environment.

Posted 7/29/2026full-timeRemote • 🇲🇽 MexicoMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Econometric and Statistical Modeling, with proficiency in Python, SQL, and Data Visualization tools like Tableau. Capable of managing complex data pipelines and ensuring product quality through best practices in a fast-paced, production environment.

Highest-signal resume keywords

Econometric ModelingData Visualization with TableauPython ProgrammingSQL QueryingCloud-Based Environments (AWS)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical AnalysisMachine LearningData Pipeline DevelopmentData InterpretationData Algorithm ManagementQuality AssuranceStatistical Tests (ANOVA, Regression)Data Warehouse StructuresData Lake StructuresProject Management

Soft Skills

Clear CommunicationProblem SolvingCritical ThinkingOrganizational SkillsTeam Collaboration

Tools & Technologies

PythonTableauRSQLPySparkGitlabCI/CD WorkflowsAWSOracleNetezza

Industry Keywords

Audience MeasurementPPM Sampling MethodologyData AnalysisProduction EnvironmentInterdepartmental Collaboration

Tech Stack

Tools & technologies

AWSCloudOraclePySparkPythonSQLTableau

About the role

Key responsibilities & impact

Research, design, develop, implement and test econometric, statistical, optimization and machine learning models.
Gain a deep understanding of the PPM (Portable People Meter) sampling methodology and become an expert in Audience Measurement, with a focus on panel management strategies.
Query data from large relational databases for various analyses and/or requests.
Utilize tools such as Python, Tableau, R etc. to perform complex data analysis and visualizations.
Oversee, maintain and evolve multiple data algorithms for managing the PPM Sample Methodology in a time-critical production environment.
Maintain and update documented departmental procedures, checklists and metrics comprehensively and on a timely basis.
Serve on various interdepartmental teams seeking to propose, evaluate and implement new initiatives, and work on other critical projects. This includes support with writing requirements, developing new software and testing.
Respond to various internal and external client requests, providing data driven solutions to the problems presented.
Incorporate quality checks to proactively detect and correct for errors throughout production processing and analysis.
Ensure product quality, stability, and scalability by facilitating code reviews and driving best practices like modular code, unit tests, and incorporating CI/CD workflows.

Requirements

What you’ll need

Bachelor’s Degree or higher in Mathematics, Statistics, Computer Science, Data Science, Data Engineering or a related field.
3+ years of professional work experience in Statistics, Data Science, and/or related disciplines, with focus on delivering analytical software solutions in a production environment.
3+ 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
Data visualization with Tableau and/or similar tools
Experience working in cloud-based environments such as AWS
DevOps tools, code management and CI/CD workflows using Gitlab or similar tools
Project management including ability to prioritize multiple assignments
Well-organized, clear communication, and an ability to handle multiple competing priorities in a fast-paced environment.
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.
Exceptional problem solving skills.
Abilities to solve problems independently and within a cross-functional team to resolve issues in the projects.
Nice to have: Working Knowledge of statistical tests and procedures such as ANOVA, Chi-squared, Correlation, Regression, and Student t-test.
Expertise in querying large datasets with SQL and of working with Oracle/Netezza/Data Warehouse and Data Lake data structures.
Critical thinking skills to evaluate results in order to make timely decisions.

Benefits

Comp & perks

Health insurance
Flexible work arrangements
Professional development