Business and Marketing Data Scientist

The US base salary range for this full-time position is $117,000 - $166,000 + 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 office & may allow for a hybrid schedule as per Google policy.
The Global Business Organization (GBO) is the engine that powers Google’s full ecosystem of products and services to help customers and partners succeed and grow. GBO includes the commercial arms of Ads sellers, business development teams, and customer services and support, as well as overlay programs that enable coordinated engagement with Google's most complex and important customers and partners.

Minimum qualifications
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The application window will be open until at least October 26, 2026. This opportunity will remain online based on business needs which may be before or after the specified date.
Minimum qualifications:
Bachelor’s degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics or a related field and 1 year of experience in the job offered or in a Business Marketing Data Scientist-related occupation.
Position requires 1 year of experience in the following: Structured programming with Python or R for data manipulation; Database querying using SQL; Statistical analysis and algorithm execution using software applications; Data extraction, manipulation, and cleaning for exploratory data analysis or extraction and manipulation of large datasets exceeding computer memory; and Explanation of statistical concepts to non-quantitative audiences.

Responsibilities
Extract, compile, and validate data across multiple sources using SQL, Python, or R while adhering to rigorous privacy and quality standards.
Collaborate with stakeholders to translate complex business goals and organizational context into tractable analysis, evaluation metrics, and statistical models.
Analyze datasets to identify product gaps, formulate conclusions, and communicate actionable insights to internal teams like engineering and product management.
Create dashboards, tools, and defined metrics to automate reporting workflows, inform product decisions, and track solution deployments.
Write and integrate production-ready solution code to resolve technical issues, validate new features, and optimize existing systems. Scale repeatable solutions through comprehensive documentation, best practice guides, tutorials, and ongoing cross-functional implementation support.