Data Scientist, Research, Power Forecasting and Optimization

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field or equivalent practical experience.
• 3 years of 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:

• 5 years of 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 experience with formulating or answering business questions using data, metrics, and quantitative algorithms.
• 4 years of experience with designing and building constraint optimization models, machine learning models, or statistical models.
• Experience in one or more of the following areas: Supply Chain Analytics, Infrastructure Capacity Planning, bin packing problems.

As a Data Scientist supporting Google’s Data Center/Power Demand Forecasting and Optimization, you will influence Google’s infrastructure investment to support Google’s Cloud/AI missions. As a trusted advisor to leaders in Technical infrastructure (TI) organization, you will play an important role in enhancing and support our decision making capabilities and making us ready for our AI first company mission.

The US base salary range for this full-time position is $127,000-$187,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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 stakeholders in cross-projects and team settings to identify and clarify business or product questions. Provide feedback to translate and refine business questions into analysis, evaluation metrics, or mathematical models.
• Use custom data infrastructure or existing data models as appropriate, using specialized knowledge. Design and evaluate models to express and solve defined problems.
• Gather information, business goals, priorities, and organizational context around the questions to answer, as well as the existing and upcoming data infrastructure.
• Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python). Format, re-structure, or validate data to ensure quality, and review the datasets.