Lead Learning Analytics Scientist - AI Education

Google; Mountain View (CA); On-site; USD 215,000 - 292,000; Full time

Job summary

Google in Mountain View, CA is hiring a Data Scientist for the Learning Frontiers team to quantify learning impact and connect AI research with pedagogy-driven product experiences across Gemini, Search, YouTube and NotebookLM.

You will analyze logs, define user personas, design experiments, and mentor team members while shaping metrics that capture educational value beyond basic engagement.

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.
• Experience with cross-functional projects and defining measurement frameworks.

Responsibilities

• Analyze logs, frame strategic questions, conduct external research, and develop roadmaps.
• Lead the development of metrics and proxies to evaluate the learning impact of AI in live experiments on Gemini, Search, and NotebookLM.
• Conduct analysis including data gathering, requirements specification, processing, deliverables, and presentations to solve non-routine problems.
• Design experiments for longitudinal analyses, and evaluate cross-product user journeys for educational purposes.
• Provide technical leadership and mentorship to team members.

Skills

Analytics

Python

R

SQL

Experimentation

Cross-functional

Communication

Education

Master's degree in Statistics/quantitative field

PhD (preferred) in quantitative field

Tools

Python

R

SQL

Databases

Job description

Google in Mountain View, CA is hiring a Data Scientist for the Learning Frontiers team to quantify learning impact and connect AI research with pedagogy-driven product experiences across Gemini, Search, YouTube and NotebookLM.

You will analyze logs, define user personas, design experiments, and mentor team members while shaping metrics that capture educational value beyond basic engagement.