Job Description

About the Team

The Global Brand Data Science team, part of Global Brand Insights & Analytics, is a critical business partner to Global Brand & Communications. We bridge technical rigor with business strategy, delivering data science solutions that drive marketing efficiency and impact. Our team is responsible for marketing revenue modeling, campaign measurement, and deep-dive analyses to uncover market opportunities. We focus on building, expanding, and automating data science capabilities, influencing top-line budget allocation and shaping strategy.

The team covers 3 key focus areas: In-app strategy & measurement, infrastructure development, and ROI Modeling. Within in-app strategy & measurement, we lead the development of measurement and prioritization frameworks, in-app campaign strategy and reporting, user cohort design (including the application of machine learning models), BLS/CLS analysis, and deep-dive insights. In our infrastructure workstreams, we build connections between internal data tools, and ingest and transform datasets to unlock new measurement and research capabilities, operationalize key processes, and democratize access to data & insights.

Responsibilities

1. Scale the marketing measurement data platform, building reliable, scalable, and unified data infrastructure to support global campaign experimentation and measurement.

2. Design, build, and manage end-to-end ETL pipelines, transforming raw ads, creative, and user interaction data into high-quality, analysis-ready datasets.

3. Develop and maintain core data models and analytical schemas, including standardized tables, derived metrics, and reusable data assets to enable consistent measurement and self-serve analytics.

4. Establish robust tracking and data linkage foundations, including exposure tracking logic, identity resolution, and URL parameter frameworks to connect marketing touchpoints with downstream user outcomes.

5. Develop data infrastructure governance and resource management, including managing HDFS/YARN queues, optimizing resource allocation, and ensuring efficient and stable usage across the data science team.

6. Ensure data quality, reliability, and scalability across systems, implementing validation, monitoring, and governance mechanisms for critical pipelines and datasets.

7. Build and maintain internal data tools, dashboards, and data services to improve efficiency, enable cross-functional alignment, and reduce manual effort across teams.

8. Partner with cross-functional stakeholders (marketing, product, research, engineering) to align on data definitions, system integration, and platform capabilities.