About the job

Our mission is to optimize the scale, speed, and efficiency of Google’s global network ecosystem through production-critical modeling and domain-native integration. As a Data Science team dedicated to delivering quantitative solutions to the most important issues in the network and its adjacent domains, we partner with stakeholders on critical initiatives and proactively solve high-impact problems through algorithms, analytics, and statistical insights. Our team manages a broad spectrum of issues ranging from network demand forecasting and probabilistic capacity planning to sophisticated risk monitoring and network operations. These efforts directly enable the network agility and scale required to support the next generation of Google's infrastructure in the AI-centric era.

As a Data Scientist in Research, you will optimize the scale, speed, and efficiency of Google’s global network ecosystem one of the world's largest and most dynamic infrastructures through production-critical modeling and analytics. You will lead quantitative projects that utilize intricate, large-scale datasets to understand global traffic usage and forecast future demand. You will be focusing in modeling the evolving impact of AI and machine learning workloads on network patterns, ensuring Google’s infrastructure remains agile and scalable in an AI-centric era. You will translate ambiguous business issues into mathematical frameworks, delivering high-quality analysis that directly influences long-term infrastructure strategy and investment decisions.

The US base salary range for this full-time position is $147,000-$211,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Work separately and deliver on difficult investigative problems end-to-end from data exploration and methodology development to communicating findings that influence infrastructure project decisions.

• Apply statistical and machine learning techniques to understand traffic usage patterns and forecast demand for Google's global network.

• Design and implement metric frameworks to monitor forecast health and quality, utilizing backtesting and historical benchmarks to extract actionable insights that guide continuous improvements in forecasting models.

• Partner with Engineering, Product Management, and Planning teams to understand business needs, frame investigative problems, and provide data-driven recommendations that guide infrastructure strategy.

• Identify strategic issues and technical bottlenecks, generating the methodologies required to resolve them and helping the team course-correct as requirements evolve.

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.

• Experience applying investigative methods to networking, cloud infrastructure, hardware supply chains, or large-scale distributed systems.

• Experience in designing and building time-series forecasting, optimization, or probabilistic models, particularly for high-scale or dynamic environments.

• Experience working at the intersection of infrastructure and data science, with a drive to solve complex, large-scale engineering issues.

• Ability to frame difficult, ambiguous business questions into mathematical problems and deliver technical solutions with minimal guidance.