Senior Data Scientist

Google•San Francisco, CA

6d•$206,600 - $237,000•Hybrid

About The Position

The 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 [https://www.google.com/about/careers/applications/benefits/]. Position reports to the Google San Francisco, CA office & may allow for a hybrid schedule as per Google policy.

Requirements

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, 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.
3 years of experience in coding in SQL, Python, or R to manipulate, analyze, visualize large datasets, and build models.
3 years of experience in statistical analysis or quantitative data synthesis to draw inferences, identify trends, and solve product or business problems.
3 years of experience in user experience measurement or attribution modeling to evaluate customer interactions.
3 years of experience in product analytics or business intelligence to define metrics, create dashboards, and measure success.
3 years of experience in stakeholder management or cross-functional collaboration to align objectives, gather consensus, and persuade leaders.

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.