Minimum qualifications: Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field. 3 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree. Preferred qualifications: 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree. About the job Google Cloud Platform (GCP) offers a suite of products ranging from Compute to Storage and ML. Operating at the forefront of AI using next-generation technology, our Cloud Analytics team focuses on understanding platform resources to provide analytical insights, improve product excellence, and support scalable growth. In this role, a primary focus of your work will involve product analytics for GCP's consumption products, such as Spot and Dynamic Workload Scheduler (DWS), analyzing and improving VM reliability to optimize overall resource usage and efficiency. Additionally, you will contribute to high-visibility projects, working cross-functionally with the Google Maps Platform (GMP) team on agent analytics while partnering with core engineering and product groups. To achieve this, you will analyze complex system logs, define key performance and reliability metrics, and apply rigorous statistical methodologies to draw insights that guide infrastructure and engineering decisions. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $147000 - $211000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities Lead analytics for cloud consumption products, such as Spot and DWS, focusing on improving VM reliability and optimizing resource efficiency. Collaborate on High-Visibility GMP Initiatives, work cross-functionally with the Google Maps Platform (GMP) engineering and product teams to develop and enhance agent analytics. Analyze complex system logs and apply rigorous statistical methodologies and next-generation AI technologies to extract insights. Establish core performance and reliability metrics to guide strategic infrastructure, product, and engineering decisions.