Senior Research Data Scientist, Network and Machines Optimization, Cloud

Google•Sunnyvale, CA

10h

About The Position

Operations and Infrastructure Data Science is a team of Data Science (Analyst and Research) experts, who provide model-based decision support to scale Google's Technical Infrastructure optimally. The AI and Infrastructure team builds and maintains the architecture, from data centers to next-generation platforms, making Google's product portfolio possible. As a Data Scientist, you will enable Google to deliver AI and Infrastructure with unparalleled scale, efficiency, reliability, and velocity which involves developing critical forecasting and capacity planning tools for Google’s technical infrastructure. You will apply investigative methods including forecasting, simulation, probabilistic cost-tradeoffs, and operations research to solve issues across Google’s internal services, Google Cloud Platform, and the hardware supply chain. Your role includes planning the machine learning, compute, and storage machines that serve Google and its customers. This aligns with the team’s mission: "Drive optimal capacity planning for machines and cluster networking by providing forecasts, models, policies, metrics and insights." You must think critically and strategically about Google’s cloud and AI as a technology, a business, and an operation. You will interface with hardware engineers on total cost of ownership, software engineers on resource optimization, and operations executives on fleet plans, all based on your analytical models. You will be leveraging data, advanced analytics, and strategic forecasting to optimize large-scale investments in technical infrastructure. The US base salary range for this full-time position is $166,000-$244,000 + 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 [https://careers.google.com/benefits/].

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.
4 years of experience in data analysis or related fields as a Statistician, Data Scientist, etc.
Experience with statistical software (e.g., R, Python, MATLAB, pandas) and database languages (e.g., SQL).

Nice To Haves

PhD degree in Data Science, Operations Research, Industrial Engineering, Statistics, or related quantitative field.
4 years of relevant work experience (e.g., as a data scientist), including experience applying advanced analytics to planning and infrastructure problems.
Experience working on technical infrastructure for cloud computing with designing and building supply chain models.
Experience in problem-framing, designing and building time-series forecasting, optimization, simulation, cost-tradeoff and probabilistic decision-making models.
Excellent problem-solving, project management skills with customer service and team collaboration skills.

Responsibilities

Develop, maintain, support, and enhance custom forecasting and capacity planning tools for Google’s machine and network infrastructure.
Collaborate with cross-functional stakeholders to understand their business needs, frame analytical problems, and identify/prioritize challenges where data science can have the biggest impact.
Drive direct analysis and modeling by articulating business questions, selecting appropriate analytical methods, and using mathematical techniques to solve business challenges.
Translate analysis results into actionable business recommendations, supported by technical documentation and presentations, and measure the resulting business outcomes.
Identify and communicate opportunities, challenges, and areas for automation that the data science group should pursue.