Senior Data Scientist, Research, Reliability Analytics

Google; Greater London; On-site; GBP 90,000 - 120,000; Full time

Job summary

Google is seeking a senior Reliability Analytics scientist to design statistical and machine learning tools that improve Platform Reliability Engineering. You will influence engineering roadmaps and work on change supervision and rollouts across Google Cloud Platform.

In partnership with engineering and product teams, you will build robust data pipelines, apply analytics to complex reliability challenges, and drive data-driven decisions that enhance user experience on Google's services.

Qualifications

• Master's degree in a quantitative field or related discipline.
• 5 years analytics experience or 3 years with PhD; coding in Python/SQL.
• Experience with statistical modeling and generative AI.
• Familiarity with data pipelines and reliability-focused analysis.

Responsibilities

• Design statistical and ML tools for reliability across platforms.
• Build investigative data pipelines for integration into infrastructure.
• Collaborate with engineering and product teams to set roadmaps.
• Influence engineering priorities and support change supervision.
• Contribute to data science initiatives and learning forums.

Skills

Analytics

Python

SQL

Statistical modeling

Generative AI

Education

Master's degree in a quantitative field

Tools

Python

SQL

Cloud computing

Job description

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.

About the job

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.

Responsibilities

• 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.

Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.