Minimum qualifications: - Bachelor's degree in Data Science, Statistics, Mathematics, Physics, Economics, Operations Research or a related field and 6 years of progressive post-baccalaureate experience in the job offered or in a Data Scientist-related occupation.
- Alternatively, will accept a Master's degree in Data Science, Statistics, Mathematics, Physics, Economics, Operations Research or a related field, and 3 years of experience in the job offered or in a Data Scientist-related occupation.
- Position requires 3 years of experience in the following: Coding in SQL, Python, or R to manipulate, analyze, visualize large datasets, and build models; Statistical analysis or quantitative data synthesis to draw inferences, identify trends, and solve product or business problems; User experience measurement or attribution modeling to evaluate customer interactions; Product analytics or business intelligence to define metrics, create dashboards, and measure success; and Stakeholder management or cross-functional collaboration to align objectives, gather consensus, and persuade leaders.
About the jobThe US base salary range for this full-time position is $206,600 - $237,000 15% bonus target equity benefits determined by role, level, and location. Individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Learn more about benefits at Google .
Position reports to the Google San Francisco, CA office & may allow for a hybrid schedule as per Google policy.
Responsibilities - Extract, format, and validate complex datasets from multiple sources to guarantee quality and analysis readiness.
- Execute advanced analyses utilizing SQL, R, and Python to resolve ambiguous problems and deliver optimal solutions.
- Report Key Performance Indicators to leadership and translate analytical results into actionable product improvement insights.
- Prototype scalable business cases and build robust processes with the foresight to anticipate future challenges.
- Master internal data structures and metrics to effectively advocate for impactful product development changes. Influence cross-functional teams to align strategic resources while providing oversight and setting standards for data scientists.
