Job Description

Minimum qualifications:

• Master's degree in Statistics, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
• Experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
• Ability to communicate in English fluently in order to contact with internal stakeholders globally.

Preferred qualifications:

• 2 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
  

About the job

At Google, data drives all of our decision-making. Quantitative Analysts work all across the organization to help shape Google's business and technical strategies by processing, analyzing and interpreting huge data sets. Using analytical excellence and statistical methods, you mine through data to identify opportunities for Google and our clients to operate more efficiently, from enhancing advertising efficacy to network infrastructure optimization to studying user behavior. As an analyst, you do more than just crunch the numbers. You work with Engineers, Product Managers, Sales Associates and Marketing teams to adjust Google's practices according to your findings. Identifying the problem is only half the job; you also figure out the solution.

Responsibilities

• Partner with stakeholders in cross-projects and team settings to identify and clarify business or product questions, providing feedback to translate them into tractable analysis, evaluation metrics, or mathematical models.
• Leverage custom data infrastructure or existing data models using specialized knowledge to design, evaluate and solve defined problems with limited precedent.
• Collect information, business goals, priorities, and organizational context around questions to answer, as well as existing and upcoming data infrastructure.
• Lead the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python), formatting, re-structuring, or validating data to ensure quality, and review the dataset to ensure it is ready for analysis.