Senior Research Data Scientist, GenAI Factuality
at Alphabet
Posted 21 hours ago
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- Compensation
- $166,000 – $244,000 USD
- City
- Not specified
- Country
- United States
Currency: $ (USD)
Join Google’s Gemini Factuality team as a Senior Research Data Scientist to ensure accuracy and quality of GenAI information. You will conduct data analysis on GenAI evals, gather and prepare data across sources, and collaborate with stakeholders to translate business questions into tractable analyses and metrics. You will help develop evaluation templates and autoraters, and work on prompting/training large language models and agentic workflows. This US-based role offers a base salary range of $166,000-$244,000 plus bonus, equity, and benefits.
Senior Research Data Scientist, GenAI Factuality
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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 experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of experience with a PhD degree.
Preferred qualifications:
- 8 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of experience with a PhD degree.
- Experience developing human evals and autoraters.
- Experience prompting/training large language models and using agentic large language model workflows.
About the job
As a Senior Research Data Scientist on the Gemini Factuality team, you will be at the center of this mission, ensuring that when users turn to Gemini for information, they receive answers that are both accurate and incredibly helpful.Responsibilities
- Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into tractable analysis, evaluation metrics, or mathematical models.
- Conduct data analysis on GenAI: evals, performer evals, training data, user logs.
- Gather information, business goals, priorities, and organizational context around the questions to answer, as well as the existing and upcoming data infrastructure.
- Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python). Separately format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.
- Evaluate development, including metrics: human eval templates, autoraters.
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