Lead Research Data Scientist, Learning Frontiers

Mountain View, CAOnsiteFull TimeLead$207,000–$300,000 /yrPosted 1 month agoVisa sponsorship available

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Required skills

Python
SQL
quantitative field
analytics
product problems
business problems
cross-functional projects
measurement frameworks
AI research
pedagogy

Nice to have

educational psychology
learning impact
digital environments
engagement metrics
junior data scientists
technical strategy

Role summary

The Lead Research Data Scientist, Learning Frontiers, will bridge AI research with pedagogy to create educational product experiences. This role involves analyzing logs, defining user personas, and evaluating experiments to measure learning impact, especially when traditional engagement metrics conflict with deep learning needs. The scientist will work with a multidisciplinary team on cutting-edge AI capabilities, building new features from the ground up. The position requires a blend of rigorous analysis and creative problem-solving to navigate data gaps and connect research goals with product surfaces. The role offers a competitive salary, bonus, and equity.

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

Preferred qualifications:

PhD in a quantitative field with a focus on pedagogy or educational psychology.
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.
Experience measuring learning impact or efficacy in digital environments, beyond standard engagement metrics.
Experience mentoring junior data scientists and guiding technical strategy for emerging product areas.
Proven ability to navigate complex, fragmented data landscapes across multiple product surfaces (e.g., Search, YouTube, Gemini).
Excellent written and verbal communication skills

About the job:

The Learning Frontiers team drives educational impact by bridging foundational AI research with pedagogy-driven product experiences. We aim to graduate successful pedagogical prototypes into primary Google surfaces - such as Gemini, Search, YouTube, and NotebookLM - to support the learner’s journey. To evolve these approaches, we need to develop measures of successful learning, especially in situations when traditional engagement metrics (like clicks) may conflict with the desirable friction required for deep learning.

As a Data Scientist on the Learning Frontiers team, you will dive deep into our logs to identify educational intent, help define user personas, and evaluate experiments that test our pedagogical hypotheses. This role requires a balance of rigorous analysis and creative problem-solving as you help us navigate data gaps and build the "connective tissue" between our research goals and our product surfaces.

This is a unique chance to join our multidisciplinary team of Product Managers, Program Managers, User Experience, Learning Science specialists, and Engineers and work on cutting-edge AI capabilities, building new features from the ground up.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

Analyze logs directly, 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.

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