Staff Research Data Scientist, Google Labs
Google· Internet & Cloud Services· Sep 16, 2026 · 1mo ago
New York, NY, USA · Mountain View, CA, USA
In office
Full-time
Staff
$180K–$250K est.
8+ years exp
Job description
Google Labs exists to discover and deliver new products that advance Google’s mission. Labs builds innovative new products and gets them into the hands of users as quickly as possible. The Labs team also runs labs.google, a place where you can try and co-create with Google's latest AI. We co-create with internal and external teams to showcase their bold and responsible ideas, so we can all shape the future of technology together (see go/labs).
In this role, you will be joining the Data Science team within Labs. The Labs Data Science team is a horizontal team, and we help the broader Labs team with metrics, experimentation, evaluation, insights, and data-driven decision making.
The Labs Data Science team is a horizontal team that supports quality evaluation, growth analysis, and data-driven decision making across the portfolio of Labs products. You will work closely with Labs product teams (PMs, Engineers, and cross-functional partners) on selected analysis projects. Example projects include growth and funnel analysis, quality evaluation (autoraters, metrics, eval set development), top-line metrics development, and user insights. You will work with these teams to develop scalable investigative tools (dashboards, metrics, pipelines), and also to communicate actionable results to the product teams to help influence product direction.
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 .
• Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced investigative methods as needed. Conduct end-to-end analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.
• Regularly share/present analysis to organization executives in order to share insights and influence product direction.
• Build and prototype analysis pipelines iteratively to provide insights at scale. Develop comprehensive understanding of Google data structures and metrics, advocating for changes where needed for both products development and sales activity.
• 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.
• Make business recommendations (e.g. cost-benefit, forecasting, experiment analysis) with effective presentations of findings at multiple levels of stakeholders through visual displays of quantitative information.
• 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.
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.
Preferred qualifications:
• 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.
• 2 years of relevant work experience as a data scientist/product analyst, including deep expertise and experience with statistical data analysis such as linear models, multivariate analysis, causal inference, sampling methods.
• Experience articulating business questions and using mathematical techniques to arrive at an answer using available data. Experience results into business recommendations.
• Demonstrated skills in selecting the right statistical tools given a data analysis problem.
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