Data Scientist II, Product - Google

About the job

Help serve Google's worldwide user base of more than a billion people. Data Scientists provide quantitative support, market understanding and a strategic perspective to our partners throughout the organization. As a data-loving member of the team, you serve as an analytics expert for your partners, using numbers to help them make better decisions. You will weave stories with meaningful insight from data. You'll make critical recommendations for your fellow Googlers in Engineering and Product Management. You relish tallying up the numbers one minute and communicating your findings to a team leader the next.

Google is an engineering company at heart. 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 users around the world. At Google, engineers not only revolutionize search, they routinely work on 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.

The US base salary range for this full-time position is $102,000-$150,000 + 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

• Perform analysis utilizing relevant tools (e.g. SQL, R, Python). Apply standard/common tools, resources, and processes to defined problems and for moderately difficult projects, execute analytical tasks with guidance.
  
• Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python). Format, re-structure, and validate data to ensure quality.
  
• Leverage a foundational understanding of broader business priorities and analytical techniques.
  
• Select appropriate approaches from clear options to address technical challenges.
  
• Develop working relationships outside of the team to contribute to cross-project collaborations and better understand data or products.