Data Scientist, Research, Power Forecasting and Optimization
Date: 1 week ago
City: Reston, VA
Contract type: Full time

Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Sunnyvale, CA, USA; Austin, TX, USA; Cambridge, MA, USA; Reston, VA, USA; San Francisco, CA, USA; Washington D.C., DC, USA.Minimum qualifications:
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 .
Responsibilities
- 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.
- 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.
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 .
Responsibilities
- 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.
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