Senior Data Scientist, Research, Reliability Analytics

Requirements:

• 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.

Nice to haves:

• 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.

What you'll be doing:

• 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.

Perks and Benefits:

• Data-driven approaches to improve decisions and systems in Platform Reliability Engineering (PRE) through data and data science.
• Opportunity to work on a mission to improve reliability in ways that matter to Google's users and customers.
• Direct impact on Google Cloud Platform reliability and deep knowledge of relevant engineering infrastructure.