Product Data Scientist, Geo
_corporate_fare_ Google _place_ Mountain View, CA, USA
Mid
Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area.
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 aboutbenefits at Google (https://www.google.com/about/careers/applications/benefits/) .
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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