Data Science Research, AI Benchmark

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:

• 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.
• Experience with statistical and quantitative modeling and forecasting, and with machine learning techniques.
• Experience with network modeling and simulation tools.
• Experience working with statistics and experiment design.

About the job

Google Research addresses challenges that define the technology of today and tomorrow. From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.

Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field -- we publish regularly in academic journals, release projects as open source, and apply research to Google products.

Responsibilities

• Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer, and provide feedback to translate and refine business questions into analysis, evaluation metrics, or mathematical models.
• Use custom data infrastructure or existing data models, 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 tools (e.g., SQL, R, Python), format, re-structure, or validate data to ensure quality, and ensure it is ready for analysis.