Senior Product Data Scientist

Minimum qualifications:

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
  
• 8 years of work experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL) (or 5 years of work experience with a Master's degree).

Preferred qualifications:

• Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
  
• 8 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.

Responsibilities

• Perform analysis utilizing relevant tools (e.g., SQL, R, Python). Using specialized knowledge, use custom data infrastructure or existing data models, as appropriate.
• Provide analytical insights and recommendations to influence the development of overall, long-term product development strategies in collaboration with the leadership team for a product/business area or the executives at a partner organization.
• Collaborate with Product Managers, Engineers, and Research Scientists to define and track key performance indicators (KPIs) for Google Ads measurement products.
• Conduct in-depth analysis of large datasets to identify trends, patterns, and insights that inform product strategy and development.
• Communicate complex data and analysis in a clear and concise manner to various stakeholders, including product leadership, engineering teams, and business partners.