Minimum qualifications:

• Bachelor's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, a related quantitative field, or equivalent practical experience.
  
• 1 year of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL), or 1 year 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.
  
• 3 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL).
  

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 will 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.

• Work with leadership teams to create solutions to identify, target and track investment shifts, mix-rate estimates and other variables around the value of initiatives to improve Maps data quality.
• Perform large-scale data analysis, modeling, time series decomposition, and more to identify opportunities for improvement and measure shifts in Engineering/Operations resource deployments.
• Work with large data sets, automate data extraction, build monitoring/reporting dashboards and use state-of-the-art infrastructure to enable scaled self-service analytics.
• Communicate user needs, make recommendations, and drive implementation for product or process changes. Identify and prioritize top challenges and key strategic growth opportunities.
• Perform analysis utilizing relevant tools (e.g., SQL, R, Python). Apply standard/common tools, resources, processes to define problems, for moderately difficult projects, and execute analytical tasks with guidance.