Data Scientist

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

The US base salary range for this full-time position is $174,000 - $253,000 + 15% bonus target + equity + benefits determined by role, level, and location. Individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Learn more about benefits at Google.

Position reports to the Google office & may allow for a hybrid schedule per Google policy.

Responsibilities

• Collaborate with cross-functional teams to define critical business questions and translate them into tractable analytical frameworks
• Gather, extract, and compile data from diverse sources using tools like SQL, R, or Python, ensuring data quality and integrity
• Design, build, and evaluate statistical models to solve complex problems and provide data-driven insights into user behavior
• Apply scientific and statistical methods to evaluate and enhance Google's products, impacting large-scale applications and user experiences
• Communicate analytical findings and model results to multidisciplinary teams to inform product development and strategy; Utilize specialized knowledge of large-scale data infrastructure and storage solutions to develop and deploy models effectively.

Qualifications

• Minimum qualifications:
  • Master’s degree in Statistics, Computer Science, Mathematics or a related field and 5 years of experience in the job offered or in a Data Scientist-related occupation.
  • Position requires 5 years of experience in the following: Analytics for solving product or business problems; Data extraction, manipulation, and cleaning; Python or R for statistical analysis and algorithms; SQL for querying and manipulating large datasets; and Application of statistical models and methods.