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Data Scientist, Research, Reliability Analytics

On-site in London·Added 1 month ago

Google

168 open roles

Overview

Requirements

  • Work on-site in London

    No relocation package mentioned.

  • Have 3+ years of experience

    Mid-level role.

Skills

Nice to have

Requirements

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 3 years of work experience with a PhD degree.
  • Experience with Python, SQL, and statistical modeling.
  • Experience working with generative AI agents.

Preferred qualifications:
  • PhD 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.
  • 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.

The role

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.

Hiring process

4 steps3 interviewsa task to do6–8 weeks in all
  1. Apply online

  2. Assessments

    Online test · Not every role

    You may be asked to take the Google Hiring Assessment (workstyle skills) and, for some roles, role-related assessments such as a coding exercise.

  3. Recruiter conversations

    With a recruiter · Phone call · 1–2 sessions

    One or two shorter conversations assessing key skills, sharing information about the organisation and team, and asking about your preferences such as location.

  4. Project work

    In your own time · Not every role

    For some roles, a small project before interviews: a case study, writing or code samples.

  5. Interviews

    With a rotating cast of Googlers

    A panel of structured interviews, over video or in person, with a rotating cast of Googlers; open-ended role-related questions scored against the same rubric for every candidate, no brainteasers.

  6. Decision and offer · With a recruiter

  • Structured interviews: every candidate for a role is scored against the same rubric; no brainteasers.
  • AI tools are not permitted during interviews.
  • For some roles, pre-selected interviews happen virtually before final interviews onsite.

About Google

Industry
Technology
Founded
1998
Employees
100,000+
Headquarters
Mountain View, USA
Website
google.com
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