Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, a related quantitative field, or equivalent practical experience.
• 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of work experience with a PhD degree.
  

Preferred qualifications:

• 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.
  
• Experience in experimental design (A/B testing), metric definition, and behavioral analysis for complex, interactive user flows.
• Experience partnering with engineering teams to build data/evaluation pipelines, with knowledge of model-based evaluation or LLM frameworks.
• Ability to operate autonomously in a highly ambiguous domain, guiding cross-functional strategy and translating high-level product goals into data science initiatives.

About the job

The team is building the future of Personal Search — a next-generation product that seamlessly bridges the gap between private data and the web. Our mission is to unlock new information retrieval, allowing users to query their own search memory explicitly or rely on intelligent, implicit assistance for highly complex tasks.

In this new era of AI, we are integrating advanced GenAI personalization directly into Search. By reducing cognitive load, saving time, and introducing moments of true user delight, we are fundamentally reshaping how people interact with information.

As a Lead Data Scientist on our Personalization team, you will lead the quality, evaluation, and measurement strategy for the next-generation Personal Search product. This is a high-impact priority role where you will bridge the gap between user experience and system engineering.

You will split your impact between two critical pillars: defining the quantitative metrics for how personalization naturally manifests in conversational flows (e.g., co-creation, proactive nudges, and interactive memory), and building the high-fidelity auto-rater infrastructure required to evaluate these complex experiences at scale.

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.

Responsibilities

• Design, validate, and scale the quantitative metrics framework for conversational personalization quality, interactive memory, and proactive user nudges.
• Develop statistical methodologies to evaluate the trade-offs between proactive AI features and user friction.
• Partner directly with Engineering to build, validate, and optimize model-based evaluation frameworks (Auto-raters) within the personalization pipeline. Drive data-driven improvements across the entire quality flywheel, seamlessly connecting data acquisition, measurement via auto-raters, dashboard monitoring, and automated prompt optimization.
• Partner closely with core UXR, Product Management, and Engineering leads to translate qualitative user insights into scalable, quantitative experimentation and product roadmaps.