Data Scientist, Energy Analytics

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, a related quantitative field, or equivalent practical experience.
  
• 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 Statistics, Engineering, Physics, Mathematics, or a 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.
  
• Experience in working or internship as an energy analyst, trader, system operator, etc., including experience applying advanced analytics to energy systems' planning, control and learning problems within universities, national labs, electric utility companies.

Experience with the energy system data.

Experience with optimization (e.g., cvx, Gurobi), statistical (e.g., R, Python) software, and database languages (e.g., SQL).

About the job

As a Data Scientist, you will collaborate with Google's Climate Operations team, Energy and Policy Strategists, Engineers and Product Managers to adjust Google's energy systems and practices based on your findings. You will apply an understanding of energy systems and market dynamics, optimization and statistical knowledge, as well as the coding experience, to develop methods to solve and assess the problems related to carbon and efficiency aware compute shifting, grid integration, energy asset forecasting and scalable control. You will work broadly with different energy stakeholders across the organization and drive innovation of our energy systems stack, while improving its efficiency and global environmental impact.

Responsibilities

• Lead projects with energy system, market analysis and carbon impact analysis, modeling, drawing from multiple problem solving methods and energy domain expertise to choose the right tool and right level of complexity appropriate for the business challenges.
  
• Engage broadly with the organization to identify, prioritize, frame, and structure and ambiguous challenges arising in the energy domain, where advanced projects or tools can have the impact.
  
• Identify and communicate the challenges and opportunities that the group should be working on.
• Articulate business questions and use techniques to arrive at an answer using data. Translate analysis results into business recommendations.
  
• Help define the problem solving direction and influence the direction of the associated engineering and infrastructure work.