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Minimum qualifications:
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative 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 3 years of work experience with a PhD degree.
Preferred qualifications:
- 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.
About the job:
Operations Data Science is a team of Data Science experts, who provide model-based decision support to grow Google's Technical Infrastructure systematically.
If you like using data, metrics, forecasts, statistics, operations research, insights and executive level business communications to guide budget decisions on Google's Technical Infrastructure, then you should consider joining Operations Data Science (ODS) as a Data Scientist.
The US base salary range for this full-time position is $174,000-$255,000 + 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.
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:
- Develop, maintain, support, and enhance custom forecasting and capacity planning tools for Google’s Datacenter infrastructure.
- Drive the analysis and modeling, drawing from multiple analytical methods and choose the right method and level of detail appropriate for the business challenges.
- Engage to identify, prioritize, frame, and structure the challenges, where data science projects or tools can have the impact. Collaborate with cross-functional stakeholders to understand their business needs and frame analytical problems.
- Articulate business questions and use mathematical techniques to arrive at an answer using data. Translate analysis results into business recommendations supported by technical documentation and presentations.
- Measure business outcomes driven from the recommendations, identify and communicate the challenges, opportunities, and automation that the group should be working on.
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