Job Details

Qualifications:

1. Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
2. At least 5 years of experience using analytics to solve product or business problems, including coding (e.g., Python, R, SQL), querying databases, or conducting statistical analysis. Alternatively, 3 years of experience with a PhD degree.

Preferred qualifications:

1. 8 years of experience using analytics to solve product or business problems, including coding (e.g., Python, R, SQL), querying databases, or conducting statistical analysis. Alternatively, 6 years of experience with a PhD degree.

About the job

Google is an engineering company that hires individuals with broad technical skills to tackle some of the greatest challenges in technology and make an impact on millions or billions of users. Data scientists at Google work on revolutionizing search, developing scalable storage solutions, large-scale applications, and new platforms for developers worldwide. From Google Ads to Chrome, Android to YouTube, and Social to Local, Google engineers are making significant technological advancements. As a Data Scientist, you will evaluate and improve Google's products, collaborating with multidisciplinary teams of engineers and analysts. Your role involves applying scientific and statistical methods to product development and enhancement, with an understanding of end-user behaviors.

Compensation

The US base salary range for this full-time position is $166,000-$244,000, plus bonus, equity, and benefits. Salary ranges are role, level, and location-dependent. Additional factors such as skills, experience, and education influence individual pay. The listed salary reflects the base only; bonuses, equity, and benefits are separate. More information about Google benefits can be found here.

Responsibilities

1. Collaborate with stakeholders across projects to identify and clarify business or product questions. Provide feedback to refine these questions into actionable analysis, metrics, or models.
2. Design and evaluate models to mathematically express and solve defined problems, utilizing custom data infrastructure or existing data models as appropriate.
3. Gather information on business goals, priorities, and organizational context, including data infrastructure details.
4. Own data collection, extraction, and compilation from various sources using tools like SQL, R, or Python. Ensure data quality and readiness for analysis through validation and re-structuring.