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Google

Data Scientist, Gmail and Chat AI Model Quality

🌎

Sunnyvale, CA, USA, Boulder, CO, USA, Kirkland, WA, USA, New York, NY, USA, Raleigh, NC, USA, Durham, NC, USA, San Francisco, CA, USA

11h ago
πŸ‘€ 1 views
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Job Description

Minimum qualifications:

  • Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
  • 13 years of work experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL) (or 10 years work experience and a Master's degree).

Preferred qualifications:

  • Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
  • 12 years of experience solving product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL).
  • Experience in extracting large sets of data with SQL and in designing ETL flows.
  • Experience working with Engineers and Product Managers, especially around providing product-centric insights.
  • Track record of solving unstructured business problems with data science, translating results into impactful business recommendations, and measuring the success of those initiatives.

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.

The Communications & Time Management (CTM) Data Science team shapes decision-making and provides actionable insights to guide product development and strategy for popular Google products like Gmail, Chat, and Calendar.

As a Data Scientist on the team, you will work closely with product and engineering teams to build products enjoyed by users globally.

This is a high-visibility role that partners closely with CTMI Product and Engineering leads. You will also regularly share your findings and present your work to Workspace leadership.

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 $214,000-$305,000 + 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.

  • Perform data exploration to understand user behavior and identify opportunities for improving GenAI models across Gmail and Chat.
  • Perform evaluations of model performance across assistive writing, summarization, and inbox highlights.
  • Define, own, and evolve model evaluation metrics and frameworks.
  • Lead the design, analysis, and interpretation of product experiments.
  • Partner with Product, Engineering, and cross-functional teams to influence, prioritize, and support product strategy.

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