Job Description
Minimum qualifications:
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
- 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.
Preferred qualifications:
- 10 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 8 years of work experience with a PhD degree.
- Familiarity with modern Machine Learning and Large Language Model (LLM) techniques.
- Ability to commit to knowledge and learning, respect for science, tolerance for ambiguity, and interest in practical application of science to business.
- Excellent collaboration skills, with the ability to collaborate cross-functionally and work effectively with Data Scientist (DS), User Experience Researcher (UXR), product and engineering partners.
About the job
In this role, you will partner cross-functionally to drive Google's mission of organizing the world’s information and making it universally accessible and useful. As technology continues to evolve, your commitment remains to help users in their journey to connect to helpful information more easily, leveraging technology to find new ways to satisfy users' information needs.
In this role, you will provide users with relevant and meaningful information based on their interests and understanding of the world’s information. Your mission is to make it effortless for people to catch up on their interests by bringing together informative, entertaining content from the voices they care about.
As a part of the Discover Data Science team, you will partner with cross-functional to drive that mission with a data-informed strategy, identifying opportunities and accelerating execution. As a part of the Discover Personalization and Quality team, you will partner with engineering and product, develop deep understanding of how to build a personalized retrieval and ranking system that delivers high-quality, compelling and valuable content. You will leverage a wide range of analysis tools (including metrics and AI raters, statistical modeling, cohort and exploratory analyses), and incorporate AI-driven understanding to define the job of a data scientist for the new era.The US base salary range for this full-time position is $207,000-$300,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. 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 GooglePlease 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 GooglePlease 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 GoogleResponsibilities
- Lead core understanding of retrieval and ranking ML systems and collaborate with engineers to deliver significant improvements that accelerate product growth.
- Analyze systems to identify opportunities while maintaining end-to-end ownership of research agenda, execution, and insight delivery to leadership.
- Build expertise in retrieval and ranking systems, advocating for changes, where needed, and driving cross-functional alignment.
- Contribute as an individual contributor, as well as a Technical Lead for a small group of data scientists.
- Manage ambiguous data science problems, and leveraging new technology to produce quantum leaps forward in understanding, such as via LLM-driven tooling.
