Minimum qualifications:

• Master's degree in Statistics or Economics, or a quantitative discipline, or equivalent practical experience.
  
• 3 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.
  

Preferred qualifications:

• PhD degree in a quantitative discipline (e.g., statistics, bioinformatics, computational biology, computer science, applied mathematics, or similar) or equivalent practical experience.
  
• 5 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
  
• 3 years of experience in statistical specialty (e.g., causal inference, econometrics, generalized linear models, experiment design, bayesian statistics, advanced forecasting, machine learning, etc.).
  
• Experience with statistical and quantitative modeling and forecasting, and with machine learning techniques.
  

About the job

At Google, Data Scientists not only revolutionize search, they routinely work on massive scalability and storage solutions, large-scale applications and entirely new platforms for developers around the world. From Google Ads to Chrome, Android to YouTube, Social to Local, Google engineers are changing the world one technological achievement after another.

As a Data Scientist, you will evaluate and improve Google's products. You will collaborate with a multi-disciplinary team of engineers and analysts on a wide range of problems. You will bring scientific accuracy and statistical methods to the challenges of product creation, development, and improvement with an appreciation for the behaviors of the end user.
The US base salary range for this full-time position is $127,000-$187,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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.
As a Data Scientist, you will evaluate and improve Google's products. You will collaborate with a multi-disciplinary team of engineers and analysts on a wide range of problems. You will bring scientific accuracy and statistical methods to the challenges of product creation, development, and improvement with an appreciation for the behaviors of the end user.
As a Data Scientist, you will evaluate and improve Google's products. You will collaborate with a multi-disciplinary team of engineers and analysts on a wide range of problems. You will bring scientific accuracy and statistical methods to the challenges of product creation, development, and improvement with an appreciation for the behaviors of the end user.

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

• Collaborate with stakeholders in cross-project and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into tractable analysis, evaluation metrics, or mathematical models.
  
• Use custom data infrastructure or existing data models as appropriate, using specialized knowledge. Design and evaluate models to mathematically express and solve defined problems with limited precedent.
  
• 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 relevant tools (e.g., SQL, R, Python). Format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.