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Data Scientist, Product, Customer Engagement

Google

$132,000 - $189,000
Aug 13, 2025
Irvine, CA, US
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Data Scientists provide quantitative support, market understanding and a strategic perspective to partners throughout the organization. As a data-loving member of the team, you serve as an analytics expert for partners, using numbers to help them make better decisions. You'll make critical recommendations for fellow Googlers in Engineering, Product Management, and User Experience.

Requirements

  • 5 years of work experience with analysis applications (e.g., extracting insights, performing statistical analysis, or solving business problems), and coding (e.g., Python, R, SQL) (or 2 years work experience with a Master's degree).
  • 5 years of experience with statistical data analysis, modeling, experimentation, and causal inference.
  • 5 years of experience with data mining, querying, and managing analytical projects.
  • 5 years of experience developing and managing metrics or evaluating programs/products.
  • Experience solving unstructured business problems with data science, translating results into business recommendations, and measuring the success of those initiatives.

Responsibilities

  • Define, own, and refine product success metrics, work with product management, UX, and engineering to develop and maintain a comprehensive metric framework that ties to business priorities and contributes to annual and quarterly OKR setting.
  • Collaborate with Engineering teams to identify and address instrumentation gaps, ensuring accurate data collection for key functionalities, with a focus on impactful customer journeys.
  • Build an understanding of the data sets used by Customer Engagement and its partner teams, and work with Engineering teams to plug gaps in logging and data infrastructure.
  • Build data aggregation and analysis pipelines, design new metrics, and create dashboards and visualizations around them.
  • Improve experimentation velocity and analysis turnaround time through adoption of self-service tools and improved processes.

Other

  • Ability to work in an unstructured environment with ambiguity.