Data Scientist, Product, Support Platform by Google

February 21, 2024
Data Scientist, Product, Support Platform by Google

Job Description

About the job:

Help serve Google’s worldwide user base of more than a billion people. Product Analysts provide quantitative support, market understanding and a strategic perspective to our partners throughout the organization. As a data-loving member of the team, you serve as an analytics expert for your partners, using numbers to help them make better decisions. You will weave stories with meaningful insight from data. You’ll make critical recommendations for your fellow Googlers in Engineering and Product Management. You relish tallying up the numbers one minute and communicating your findings to a team leader the next.

Minimum qualifications:

  • Bachelor’s degree or equivalent practical experience.
  • 3 years of experience working with statistical packages (e.g., R, SAS, Stata, MATLAB, etc.).
  • Experience in articulating product questions, pulling data from datasets (e.g., SQL), and using statistics.

Preferred qualifications:

  • Experience in experimental design (e.g., A/B, multivariate, Bayesian methods) and incrementality analysis.
  • Experience in utilizing unstructured data to answer open-ended business questions.
  • Experience in identifying opportunities for business improvement and defining/measuring the success of those initiatives.
  • Experience working with large and multiple datasets/data warehouses.
  • Ability to retrieve data sets using relevant tools and coding.
  • Excellent communication and presentation skills, with the ability to deliver findings of analysis.


  • Build models and frameworks to better understand the opportunity size of Customer Engineers (CE) initiatives, enable more informed decisions,  and contribute to annual and quarterly OKR setting.
  • Build an understanding of the data sets used by CE and 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.