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Minimum qualifications:Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, a related quantitative field, or equivalent practical experience.3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.2 years of experience as a data scientist or applied scientist in an industry setting.Preferred qualifications:5 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.Experience in marketplaces and at-scale consumer products. 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.As a key member of the team, you work with engineers to analyze and interpret data, develop metrics to measure results and integrate new methodologies into existing systems.The Search Ads and Google Experience (SAGE) organization supports developing the most important Ad products at Google, from classic text ads, to rich shopping ads, to exciting new products like Discovery ads. These products are the heart of Google’s business are complex, advanced, and they are rapidly growing and evolving.Users come first at Google. Nowhere is this more important than on our Advertising and Commerce team: we believe that ads and commercial information can be highly useful to our users if that information is relevant to what our users wish to find or do. Advertisers worldwide use Google Ads to promote their products; publishers use AdSense to serve relevant ads on their website; and business around the world use our products (like Google Shopping, and Google Wallet) to support their online businesses and bring users into their offline stores. We are constantly innovating to deliver the most effective advertising and commerce opportunities of tomorrow.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 for new hire 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. 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 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 specialized knowledge. Design and evaluate models to mathematically express and solve defined problems with limited precedent.Gather information, business goals, priorities, and organizational context around the questions to answer, as well as the existing and upcoming data infrastructure.Own the process of gathering, extracting, and compiling 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.