Senior Data Scientist, Research, Reliability Analytics

The Reliability Analytics Team is on a mission to improve decisions and systems in Platform Reliability Engineering (PRE) through data and data science. We address a broad spectrum of reliability problems where data-driven approaches can be applied, combining subject matter expertise with statistical, predictive, and generative AI/ML methods to improve reliability in ways that truly matter to Google's users and customers. In this role, you will design statistical and machine learning tools, influencing engineering priorities and roadmaps in key areas like software rollouts and change management. You will have a direct impact on Google Cloud Platform reliability, gaining deep knowledge of relevant engineering infrastructure and data assets to solve complex issues. Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible.

Design and build statistical, machine learning, and AI tools, alongside investigative data pipelines, for integration into existing or new reliability infrastructure. Apply data science to improve Google Cloud Platform reliability in partnership with the Platform Reliability Engineering (PRE) organization, focusing on change supervision and rollouts. Collaborate closely with engineering and product teams to build and improve reliability tools according to engineering priorities. Gain deep knowledge of relevant engineering infrastructure and data assets to influence engineering roadmaps and priorities. Contribute to team-wide learning forums and broader data science initiatives.

Minimum qualifications: Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field. 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 3 years of work experience with a PhD degree. Experience with Python, SQL, and statistical modeling. Experience working with generative AI agents. Preferred qualifications: 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree. Experience in software development and source control methodologies. Experience working with cloud computing or distributed computing environments. Demonstrated ability to build investigative tools and data pipelines for infrastructure integration. Proven track record of influencing engineering priorities and roadmaps.