Minimum qualifications: Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related field, and 3 years of experience in the job offered or in a Data Scientist- related occupation. Alternatively, will accept a PhD degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related field. Position requires: Using analytics to solve product or business problems; Coding in SQL; Coding in Python or R; Querying databases or statistical analysis; Analysis applications involving extracting insights and performing statistical analysis; One of the following: Experiment design, Mathematical optimization, Machine Learning, User experience measurement, or Measurement and attribution. About the job The US base salary range for this full-time position is $225,100 - $252,000 + 15% bonus target + equity + benefits determined by role, level, and location. Individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Learn more about benefits at Google. Position reports to the Google Mountain View, CA office & may allow for a hybrid schedule as per Google policy. Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale. Responsibilities 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. Gather, extract and compile data across sources via relevant tools (e.g., SQL, R, Python); and Independently format, re-structure, and/or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.