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Product Data Scientist, Geo

Job in Mountain View, Santa Clara County, California, 94039, USA
Listing for: Socket.dev
Full Time position
Listed on 2026-08-10
Job specializations:
  • IT/Tech
    Data Analyst, Data Scientist, Data Engineering, Data Science Manager
Salary/Wage Range or Industry Benchmark: 138000 - 197000 USD Yearly USD 138000.00 197000.00 YEAR
Job Description & How to Apply Below

Minimum qualifications:

  • Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
  • 5 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL) or 2 years of experience with a Master's degree.
Preferred qualifications:
  • Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
  • 5 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL).
About the job:

Geo Data Science (DS) Data and Contributions operates within the centralized Geo Data Science team. In this role, you will be collaborating closely with Product Managers, Engineers, Program Managers, and Operations teams across the Geo Platform, and will focus on key issues surrounding map data quality, user interaction with spatial data, and the relative impact of manual versus automated edits. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $138000 - $197000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:
  • Automate data extraction from extensive geospatial datasets to build comprehensive reporting and monitoring dashboards that track business health.
  • Conduct data modeling and analysis to discover improvement opportunities and accurately evaluate initiative impact.
  • Design and develop statistical methodologies to evaluate data quality while performing thorough root cause analysis on failure cases.
  • Present data-driven findings and insights to key stakeholders in regular syncs, making recommendations to lead product or process enhancements.
  • Identify and prioritize growth opportunities and critical operational issues, driving execution and noticeable improvements based on continuous feedback.
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