Senior Research Data Scientist, Resource Planning, Google Cloud

Google • Sunnyvale, CA

6h • $166,000 - $244,000

About The Position

In this role, you will apply operations research and statistical methods to solve challenges for both Google’s internal services and Google Cloud Platform. You will work broadly 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. You will think critically and strategically about Google’s cloud as a technology, a business, and as an operation. You will be 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. You will contribute to the development of critical forecasting and capacity planning tools for Google storage products, as well as other resource efficiency initiatives. Through this work, you will not only help Google run as efficiently as possible, but you will also help us to advance the statistical forecasting for resource planning. 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 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.
• 3 years of experience developing/deploying machine learning and time series forecasting models using statistical software (Python, R).

Nice To Haves

• PhD in Operations Research, Industrial Engineering, Statistics, or a related field.
• 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.
• 4 years of work experience (e.g., as a data scientist), including applying advanced analytics to planning and infrastructure problems.
• Experience designing and building machine learning and statistical forecasting models.
• Excellent problem-framing, problem-solving, project management, customer service, and team collaboration skills.

Responsibilities

• Develop, maintain, support, and enhance forecasting and capacity planning tools for centralized storage product 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.
• Identify and communicate the challenges and opportunities that the group should be working on. Help define the analytical 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. Translate analysis results into business recommendations.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level