Staff Data Scientist, Search Community

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field, or equivalent practical experience.
• 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 experience with a PhD degree.
• Experience designing A/B tests, analyzing experimental results to validate assumptions, measuring product efficacy, and informing product decisions.
• Experience working with consumer-facing technical products.

Preferred qualifications:

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

About the job

The Community Data Science team is an integral part of the Search Communityorganization - our mission is to bring fresh, authentic, and relatable content to the Google Search Page by harnessing User Generated Content (UGC) created on third-party social media platforms and Google properties, as well as authoritative publishers content from traditional media. We leverage Google's AI foundation to select, organize, summarize, and ultimately make UGC content more easily consumable on SRP. We bring this content to Search results directly via Community-owned features and indirectly by serving curated UGC content with other teams and SRP features.

Our work encompasses three main areas: product partnership, strategic analysis, and metrics. We work with our Product and Engineering leads to launch innovative features on the Google Search Page that bring authentic and relatable human perspectives to Search results. We seek to understand how our users consume UGC on our surfaces and third-party platforms and how Google can be more useful to our users in a world where so much content is created on social platforms, yet finding what is truly helpful is challenging. We design new metrics critical to guiding our decisions and measuring progress toward our goals.

In Google Search, we're reimagining what it means to search for information - any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.

The US base salary range for this full-time position is $177,000-$266,000 + 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

• Collaboratewith Engineering and Product teams to design and execute A/B tests, analyze experimental results to validate assumptions, measure product efficacy, and inform launch decisions.
• 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.
  
• Conductheadroom analyses to assess the potential impact of new product concepts and contribute to project prioritization.