Senior Data Scientist, Google Research
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Minimum qualifications:
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
- 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 one or more areas of statistical or Machine Learning (ML) modeling, experimentation or survey design, building and reading from logs-based pipelines.
- Experience in research environments, publications or open-sourcing.
- Experience with human and automated evaluation of machine learning system.
About the job
The Google Research Data Science team is a centralized team that supports a variety of efforts within Google Research, aimed at catalyzing the adoption of human-centered AI across several product collaborations and in health, education and internationalization.
As a Data Scientist on the Google Research team, you will own the data science support for a variety of research projects, collaborating closely with engineers, researchers, and product managers. You will be evaluating and measuring the quality and impact of product prototypes, features, and improvements. This includes designing and implementing evaluation frameworks, analyzing data to generate actionable insights, and communicating findings to audiences.
Google Research addresses challenges that define the technology of today and tomorrow. From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field -- we publish regularly in academic journals, release projects as open source, and apply research to Google products.
Responsibilities
- Develop comprehensive evaluation strategies for a range of AI applications. Define appropriate metrics and inform algorithm development with quantitative methods.
- Conduct end-to-end evaluations for new features and prototypes, ensuring alignment with human-centered AI principles.
- Translate complex analysis results into actionable recommendations for researchers and product teams and provide consultations to teams across Google Research on various aspects of research design and analysis, including study design, experiment setup, label creation, and statistical methodology.
- Collaborate with the evaluation infrastructure, product and research scientists and engineering teams to improve and standardize human and Large Language Model (LLM) based evaluation processes. This includes refining questions, metrics, and methods.
- Contribute to the team's growth and efficiency by creating reusable code, libraries, and collaboration such as develop a Python library for automating common evaluation tasks, enabling faster and more consistent results.
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