Data Scientist II — Audience Analytics & Forecasting

Nielsen; Ciudad de México; Presencial; MXN 800.000 - 1.100.000; Jornada completa

Descripción de la vacante

Nielsen is seeking an experienced data scientist to contribute to forecasting, modeling, and code that power our PPM Wearables audience measurement panels. You will build data pipelines, run analyses, and present data-driven recommendations to stakeholders across teams.

The role focuses on econometric and ML models, SQL data querying, and deploying scalable analytics in a production environment using Python, Tableau, and cloud-based tools like AWS.

Formación

• Bachelor’s degree in Mathematics, Statistics, CS, or related field.
• 3+ years in Statistics/Data Science delivering analytical software in production.
• Proficient in Python, PySpark and SQL for data pipelines.

Responsabilidades

• Develop econometric, statistical, optimization and ML models.
• Understand PPM sampling methodology and audience measurement strategies.
• Query data from large relational databases for analyses and requests.
• Use Python, Tableau, R to perform complex data analyses and visualizations.
• Maintain data algorithms for PPM Sample Methodology in production.
• Document procedures, checklists and metrics regularly.
• Collaborate with cross-functional teams to propose and test new initiatives.
• Respond to client requests with data-driven solutions.
• Incorporate quality checks to detect and correct errors in production.
• Promote modular code, unit tests, and CI/CD practices.

Conocimientos

Python

SQL

PySpark

Tableau

AWS

CI/CD

Data analysis

GitLab

Educación

Bachelor's in Mathematics

Bachelor's in Computer Science

Herramientas

Oracle

Netezza

Data Warehouse

Tableau Server

GitLab

Descripción del empleo

Nielsen is seeking an experienced data scientist to contribute to forecasting, modeling, and code that power our PPM Wearables audience measurement panels. You will build data pipelines, run analyses, and present data-driven recommendations to stakeholders across teams.

The role focuses on econometric and ML models, SQL data querying, and deploying scalable analytics in a production environment using Python, Tableau, and cloud-based tools like AWS.