Data Scientist

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

The US base salary range for this full-time position is $163,000 - $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 Cambridge, MA office & may allow for a hybrid schedule as per Google policy.

Responsibilities:

• Conduct end-to-end analysis using tools like SQL, R, or Python to solve ambiguous business problems and guide decision-making.
• Influence long-term product development strategies by providing analytical recommendations to leadership and key partner organizations.
• Collaborate with stakeholders to define business questions, develop analytical roadmaps, and identify optimal methodologies and data requirements.
• Define and report on Key Performance Indicators for business reviews, translating complex analytical results into actionable product improvement opportunities.
• Build and prototype new analytical processes, models, and business cases iteratively to provide scalable insights and advocate for data infrastructure improvements. Oversee data gathering processes and review analyses from team members to ensure data quality, provide expert feedback, and address analytical challenges.

Minimum qualifications:

• Master’s degree in Statistics, Applied Mathematics, Computer Science, Business Analytics, Economics or a related field, and 2 years of experience in the job offered or in a Data Scientist-related occupation.
• Position requires 2 years of experience in the following: Data extraction, manipulation, cleaning, and validation using a query language; Statistical analysis and modeling using a programming language such as Python or R); Translating open-ended business problems into structured analytical frameworks and synthesizing data to create strategic action plans; Defining and rationalizing metrics to measure business outcomes; and Communicating and presenting analytical findings to influence stakeholder decisions and senior leadership.