Data Scientist III, Research, Google Commerce

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field or equivalent practical experience.
• 3 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
  

Preferred qualifications:

• 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
  
• Excellent skills in implementing statistical analysis and data wrangling using Python and SQL.

In this role, you will evaluate and improve Google's Commerce products. You will collaborate with a multidisciplinary team of engineers and analysts on a wide range of problems. You will bring investigative aptitude and statistical methods to the challenges of measuring quality, improving consumer products, and understanding the behavior of our end users, merchants, and partners. You will also develop, organize and launch experiments and projects that cross Engineering, Product Management, Business, and Marketing teams.

The US base salary range for this full-time position is $127,000-$187,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

• Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into tractable analysis, evaluation metrics, or mathematical models.
  
• Use custom data infrastructure or existing data models as appropriate, using knowledge. Design and evaluate models to mathematically express and solve defined problems with limited precedent.
• Build and prototype analysis pipelines to provide insights at scale. Develop a comprehensive understanding of Google data structures and metrics, advocating for changes for both product development and business activity.
• Own the process of gather, extract, and compile data across sources via relevant tools (e.g., SQL, R, Python). Format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.