Staff Data Scientist, ML Efficiency Data Science

About the position

Google's homegrown, bespoke ML TPU infrastructure is one of Google's fastest growing infrastructure investments, which enables increase in performance despite the end of Moore's Law. ML Efficiency Data Science is the team in Google Cloud that provides insights, tools and analyses that help ML infrastructure service consumers (e.g., product area users can effectively use ML resources for training and serving ML models). Our mission is to deliver a common infrastructure as a service for machine learning that is easy to use, efficient and robust using analytics services to drive actionable insights for ML. And we are searching for people who are passionate about this space and are looking to make a significant impact. 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.

Responsibilities

• Perform analysis utilizing relevant tools (e.g., SQL, R, Python). Provide analytical thought leadership through proactive and strategic contributions (e.g., suggests new analyses, infrastructure or experiments to drive improvements in the business).
• Own outcomes for projects by covering problem definition, metrics development, data extraction and manipulation, visualization, creation, and implementation of analytical/statistical models, and presentation to stakeholders.
• Develop solutions, lead, and manage problems that may be ambiguous and lacking clear precedent by framing problems, generating hypotheses, and making recommendations from a perspective that combines both, analytical and product-specific expertise.
• Oversee the integration of cross-functional and cross-organizational project/process timelines, develop process improvements and recommendations, and help define operational goals and objectives.
• Oversee the contributions of others and develop colleagues' capabilities in the area of specialization.

Requirements

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
• 10 years of experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL), or 8 years of experience with a Master's degree.

Nice-to-haves

• Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
• 12 years of experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL).

Benefits

• Bonus
• Equity
• Benefits