Research Data Scientist, Network and Machines Optimization, Cloud

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.
  
• Experience with statistical and data analysis software (e.g., R, Python, MATLAB, pandas) and database languages (e.g., SQL).

Preferred qualifications:

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

Operations 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.

In this role, you will help Google deliver Artificial Intelligence (AI) and Infrastructure at unparalleled scale, efficiency, reliability and velocity by contributing to the development of critical forecasting and capacity planning tools for Google’s technical infrastructure. You will apply investigative methods (forecasting, optimization, simulation, probabilistic cost-tradeoffs and decision-making, operations research) to solve issues for Google’s internal services, Google Cloud Platform, and Google’s hardware supply chain. You will plan the machine-learning, compute, storage machines that train and serve all of Google and its customers, as part of a team whose mission is to: “Drive optimal capacity planning for machines and cluster networking by providing forecasts, models, policies, metrics and insights.” You will think critically about Google’s cloud and AI as a technology, a business, and as an operation. You will be comfortable discussing total cost of ownership with hardware engineers, resource optimization with software engineers, and reviewing fleet plans and deployment policies with operations executives, all based upon the investigative models that you will be developing.

Behind everything users see online is architecture built by AI and Infrastructure teams 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.

The US base salary range for this full-time position is $141,000-$202,000 + 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.

• Collaborate with cross-functional stakeholder to understand their business needs and frame investigative problems.
• Develop, maintain, support, and enhance custom forecasting and capacity planning tools for Google’s machine and network infrastructure.
• Drive direct analysis and modeling, drawing from multiple investigative methods and choose the right method and level of complexity appropriate for the business issues.
• Engage broadly to identify, prioritize, frame, and structure ambiguous issues, where data science projects or tools can have the biggest impact. Articulate business questions and use mathematical techniques to arrive at an answer using data.
• Translate analysis results into actionable business recommendations supported by technical documentation and presentations. Measure business outcomes driven from the investigative recommendations. Identify and communicate the issues, opportunities, and automation that the group should be working on.