Data Scientist

• Collaborate with stakeholders to translate business needs into clear analytical questions, evaluation metrics, and mathematical models.
• Gather business goals, organizational context, and information regarding existing and upcoming data infrastructure to guide your analysis.
• Extract and compile large-scale data from multiple sources using tools such as SQL, R, and Python.
• Validate and prepare datasets for analysis by formatting, re-structuring, and ensuring data quality and integrity.
• Design and evaluate sophisticated models to mathematically solve complex problems with limited precedent. Apply scientific and statistical methods to evaluate and improve Google's products for millions of users worldwide.

Minimum qualifications:

• Master’s degree in Computer Science, Statistics, Mathematics, Data Science, Economics, Physics, Operations Research, or a related field and 3 years of experience in the job offered or in a Data Scientist-related occupation.
• Alternatively, will accept a Ph.D. in Computer Science, Statistics, Mathematics, Data Science, Economics, Physics, Operations Research, or a related field.
• Position requires: Developing metrics and analyzing data to support strategic decisions; Building statistical machine learning models for both offline analysis and production-scale applications; research new methods for experimentation and statistical inference; Python or R for statistical analysis and algorithms; SQL for querying databases; and Presenting analyses and visualizing data from experiments, forecasting, and model prediction.

• Master’s degree in Computer Science, Statistics, Mathematics, Data Science, Economics, Physics, Operations Research, or a related field and 3 years of experience in the job offered or in a Data Scientist-related occupation.
• Alternatively, will accept a Ph.D. in Computer Science, Statistics, Mathematics, Data Science, Economics, Physics, Operations Research, or a related field.
• Position requires: Developing metrics and analyzing data to support strategic decisions; Building statistical machine learning models for both offline analysis and production-scale applications; research new methods for experimentation and statistical inference; Python or R for statistical analysis and algorithms; SQL for querying databases; and Presenting analyses and visualizing data from experiments, forecasting, and model prediction.