Staff Data Scientist, Product

Minimum qualifications:

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

Preferred qualifications:

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

About the job:

Help serve Google's worldwide user base of more than a billion people. Data Scientists provide quantitative support, market understanding and a strategic perspective to our partners throughout the organization. As a data-loving member of the team, you serve as an analytics expert for your partners, using numbers to help them make better decisions. You will weave stories with meaningful insight from data. You'll make critical recommendations for your fellow Googlers in Engineering and Product Management. You relish tallying up the numbers one minute and communicating your findings to a team leader the next.

Google is an engineering company at heart. We hire people with a broad set of technical skills who are ready to take on some of technology's greatest challenges and make an impact on users around the world. At Google, engineers not only revolutionize search, they routinely work on scalability and storage solutions, large-scale applications and entirely new platforms for developers around the world. From Google Ads to Chrome, Android to YouTube, social to local, Google engineers are changing the world one technological achievement after another.

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.
  
• Directly or indirectly oversee the contributions of others and develop colleagues’ capabilities in the area of specialization.