Data Scientist, University Graduate, 2025

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.

As a Data Scientist, you will evaluate and improve Google's products. You will collaborate with a multi-disciplinary team of Engineers and Analysts on a wide range of problems, using statistical methods for the issues of measuring quality, improving consumer products, and understanding the behavior of end-users, advertisers, and publishers.

Responsibilities

• Work with large complex data sets, solve difficult non-routine analysis problems, and apply advanced problem-solving methods as needed. Conduct analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.
• Make business recommendations (e.g., cost-benefit, forecasting, experiment analysis) with effective presentations of findings at multiple levels of stakeholders through visual displays of quantitative information.
• Research and develop analysis, forecast and optimize methods to improve the quality of Google's user facing products such as ads quality, search quality, end-user behavioral modeling and live experiments.
• Help suggest, support and shape new data-driven and privacy-preserving advertising and marketing products in collaboration with engineering, product and customer-facing teams.
  
• Find ways to combine large-scale experimentation, statistical-econometric, machine learning and social-science methods to answer business questions at scale.