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Open nowFirst seen 4 hours ago

Research Data Scientist, Learning Frontiers

Google3,138 open roles

Where
Mountain View, CA, USA; New York, NY, USA
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Your applicationOpen nowResearch Data Scientist, Learning FrontiersGoogle · Mountain View, CA, USA; New York, NY, USA

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The clock on this job

Early applications get read.

8.0% of postings close within 7 days. Measured by our own scanner across the market. Google postings stay open a median of 26 days.

Share of postings closed within
  1. 1.6%1 day
  2. 3.6%3 days
  3. 8.0%7 days
  4. 15.0%14 days
  5. 34.2%30 days
This job: first seen 4 hours ago

Google median: 26 days open

The posting

Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; New York, NY, USA.

Minimum qualifications:

  • Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
  • 3 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
  • 3 years of experience in data analysis, including experience with statistical modeling or hypothesis testing.
  • Experience creating data visualizations and communicating technical findings to non-technical stakeholders.

Preferred qualifications:

  • 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 a PhD degree.
  • Experience in the education technology (EdTech) sector or academic research environments.
  • Experience building and maintaining data pipelines and documentation for scalability.
  • Interest in using AI to create transformative educational experiences.

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: $147000 - $210000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

  • Work with large, complex data sets to analyze experiments and interpret results to optimize educational features across Gemini, Search, NotebookLM, experimental products, and solve difficult, non-routine analysis problems.
  • Develop and maintain documentation for data pipelines and analysis workflows to ensure research is scalable and reproducible.
  • Conduct exploratory data analysis to identify and address gaps in education-related logging and user identification.
  • Interact cross-functionally with a wide variety of people and teams. Work closely with engineers to identify opportunities for, design, and assess improvements to google products.
  • Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python). Independently format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.
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