As a Data Science team that supports Core Ranking and AI Context Engineering (CRAFT), we are the foundation for all of Google’s key products - Search, AI Overview, and AI Mode. Working alongside engineers and product managers, we work on the most complex challenges, push the boundaries of Web and AI quality, and architect novel ways for billions of users to experience Google’s capabilities on a daily basis. This is your chance to define our Search products for Google.
The mission of the CRAFT team is to drive measurements, insights, and impel actions with our partners across Core Ranking and AI Context Engineering in Search to elevate Google's Search Engine Results Page (SRP) and AI Mode and AI Overviews (AIX) products.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $253000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Work closely with engineers and product managers to identify quality and metric headrooms.
- Conduct rigorous analyses of large-scale datasets, apply statistical/AI methods to solve complex problems, and present actionable insights and recommendations to stakeholders.
- Develop evals and measurements to guide hillclimbing and iterative improvements.
- Be an integrated partner to impel system changes and launches.
- Automate such evals and measurements in collaboration with engineering and product.
Qualifications
Minimum qualifications:
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
- 5 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of experience with a PhD degree.
Preferred qualifications:
- 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 working on a consumer facing product or quality evaluations.
- Experience with experimental design and analysis, survey design, evaluation methodologies, statistical modeling and machine learning algorithms.
- Ability to convey complex information clearly and concisely, both verbally and in writing.
- Ability to apply quantitative models to real-world business problems.
- Ability to learn new skills and adapt to changing environments, including track record of self-directed learning and knowledge application.