Data Scientist, Cloud Planning and Profitability - Google
Sunnyvale, TX 75182
About the Job
Minimum qualifications:Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field, 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, or 3 years of work experience with a PhD degree.5 years of experience building statistical demand forecasting models
Preferred qualifications:8 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degreeExperience with demand forecasting and supply chain planning in the cloud industry.Experience with infrastructure/high-tech business-to-business (B2B) pricing or business strategy
About the job Google is and always will be an engineering company
We hire people with a broad set of technical skills who are ready to take on some of technology's greatest challenges and make an impact on millions, if not billions, of users
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 $150,000-$223,000 + 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
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)
Independently format, re-structure, and/or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.
Preferred qualifications:8 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degreeExperience with demand forecasting and supply chain planning in the cloud industry.Experience with infrastructure/high-tech business-to-business (B2B) pricing or business strategy
About the job Google is and always will be an engineering company
We hire people with a broad set of technical skills who are ready to take on some of technology's greatest challenges and make an impact on millions, if not billions, of users
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 $150,000-$223,000 + 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
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)
Independently format, re-structure, and/or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.
Source : Google