Product Data Scientist, Operations

Minimum qualifications:

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
  
• 5 years of experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL) or 2 years of experience with a Master's degree.

Preferred qualifications:

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

About the job

Our team's mission is Data Science to support growing and running an efficient, reliable cloud. We focus primarily on the Google Compute Engine (GCE) and Vertex products. We are part of a larger Data Science organization that supports planning and analysis for Google's global infrastructure. We're excited and have some early successes bringing AI to our work.The US base salary range for this full-time position is $138,000-$198,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. 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.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Work with product leaders to understand objectives and formulate quantifiable business questions.
• Create and evaluate metrics to reflect the status of the business.
• Deliver insights into the data by creating workflows, colabs, dashboards and other visualizations.
• Partner with engineering teams to understand, design and improve data sources and applications.
• Perform analysis by utilizing relevant tools (e.g., SQL, R, Python). Use comprehensive technical knowledge, custom data infrastructure or existing data models.