Google
Product Data Scientist, Operations
Found: Today
Product Data Scientist, Operations
About the job
Operations Data Science (ODS) is a team of Data Scientists (Research and Analyst) experts, who provide model-based decision support to scale Google's Technical Infrastructure optimally.
If you like using data, metrics, forecasts, statistics, operations research, analytical insights, strategic thinking and executive level business communications to optimize large spend decisions on Google's technical infrastructure, then you should consider joining ODS as a Data Scientist.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $138000 - $198000 (USD) + 15% bonus target + equity + benefitsLearn more about benefits at Google.Minimum qualifications:
- Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
- 5 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL) (or 2 years work experience and a Master's degree).
Preferred qualifications:
- Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
- 5 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL).
Responsibilities
- Collaborate with cross-functional stakeholders to understand their business needs and frame analytical problems.
- Develop, maintain, support, and enhance custom forecasting and capacity planning tools for Google’s data center infrastructure.
- Drive analysis and modeling, drawing from multiple analytical methods and choose the right method and level of complexity appropriate for the business challenges.
- Engage broadly to identify, prioritize, frame, and structure complex and ambiguous challenges, where data science projects or tools can have the biggest impact. Measure business outcomes driven from the analytical recommendations.
- Articulate business questions and use mathematical techniques to arrive at an answer using data. Translate analysis results into actionable business recommendations supported by technical documentation and presentations.
