Senior Data Scientist, Resource Efficiency

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, a related quantitative field, or equivalent practical experience.
• 5 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of experience with a PhD degree.
• Experience with statistical data analysis and experimental design.

Preferred qualifications:

• 8 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of experience with a PhD degree.
• 4 years of experience as a data scientist, including applying advanced analytics to planning and infrastructure problems.
• Experience designing and building machine learning models.
• Experience with optimization models.
• Excellent problem-framing, problem-solving and project management skills.

At Google, data drives all of our decision-making. Quantitative Analysts work all across the organization to help shape Google's business and technical strategies by processing, analyzing and interpreting huge data sets. Using analytical excellence and statistical methods, you mine through data to identify opportunities for Google and our clients to operate more efficiently, from enhancing advertising efficacy to network infrastructure optimization to studying user behavior. As an analyst, you do more than just crunch the numbers. You work with Engineers, Product Managers, Sales Associates and Marketing teams to adjust Google's practices according to your findings. Identifying the problem is only half the job; you also figure out the solution.

As a key member of the team, you work with engineers to analyze and interpret data, develop metrics to measure results and integrate new methodologies into existing systems.

As member of the Resource Efficiency Data Science team, you will apply operations research and statistical methods to solve challenges related to compute, storage, network, and data center capacity for Google’s internal services and Google Cloud Platform. You will work across Google’s Platforms Engineering, Systems Infrastructure, and Site Reliability Engineering teams to optimize our deployment of resources and drive innovation in our software stack, allowing for efficient use of resources.

As a Senior Data Scientist, you will think strategically about Google’s cloud as a technology, a business, and as an operation. You will be comfortable discussing total cost of management with hardware engineers, resource optimization with software engineers, or reviewing fleet plans and deployment policies with operations executives, all based upon the analytical models that you will be developing.

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.

• Develop, maintain, support, and enhance custom forecasting and capacity planning tools for Google Product Area resource planning.
• Lead projects with analysis and modeling, drawing from multiple analytical methods to choose the right tool and right level of complexity appropriate for the business challenges.
• Engage broadly with the organization to identify, prioritize, frame, and structure complex and ambiguous challenges, where advanced analytics projects or tools can have the biggest impact.
• Help define the direction and influence the direction of the associated engineering and infrastructure work.
• Articulate business questions and use mathematical techniques to arrive at an answer using data and translate analysis results into business recommendations.