Research Data Scientist, Cloud Demand Forecasting and Capacity Planning
Google· Internet & Cloud Services· Sep 17, 2026 · 1mo ago
Atlanta, GA, USA · Thornton, CO, USA
In office
Full-time
Mid
$117K–$176K est.
3+ years exp
Job description
Drive the inventory efficiency, obtainability, and growth of Google Cloud’s compute, storage, and ML products through scalable data science solutions for forecasting organic and inorganic demand, planning and managing capacity, and developing product innovations.
As a data scientist on the CCDS (Cloud Capacity Data Science) team, you will develop, maintain, and improve forecasting models and capacity solutions to support Cloud's business objectives. Our customers want a high-quality experience when obtaining and using Google Cloud's infrastructure to run their workloads. Your challenge on most projects will be to enable Cloud to efficiently use its infrastructure and to ensure a high-quality obtainability experience for our customers. You will deploy and contribute to advanced machine-learning models that forecast the organic and inorganic demand for our many Cloud products. In this role, you will also use advanced operations research methods to develop algorithms that recommend actions based on our forecasts of future demand. In addition, you will collaborate with a larger multi-disciplinary team of engineers, program managers, and product managers to optimize our fast-growing fleet's massive scale and flexible configuration.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google .
• Develop, maintain, and improve forecasting models and capacity planning solutions to support Cloud's business objectives.
• Make efficient use of Cloud's infrastructure, while achieving service level objectives for Cloud's customers.
• Make larger, mostly independent, technical contributions by consistently executing and finishing end-to-end tasks towards a larger goal with minimal assistance from team members.
• Generate the methodologies required to solve ambiguous problems, and take ownership of the solution, often involving many different activities beyond analysis such as supporting launches, working cross-functionally, and creating documentation.
• Communicate and work with engineers and subject matter experts to become fully integrated with a cross-disciplinary team. Demonstrate working knowledge of data science and related technical areas of the organization, identified as a Google individual contributor by team organizers and leaders.
• 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.
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:
• PhD in Operations Research, Industrial Engineering, Statistics or related 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 a PhD degree.
• 4 years of relevant experience (e.g., as a data scientist), including experience applying advanced analytics to planning and infrastructure problems.
• Experience designing and building statistical forecasting models.
• Experience designing and building machine learning models.
• Excellent problem-framing, problem-solving and project management skills.
Application prep
Use the role’s language and application details while tailoring your materials.
Keywords to mirror
mlpythonsqlmachine learning
- Platform
- Effort
- medium