Senior Research Data Scientist at Google, Mountain View, CA
Senior Research Data Scientist at Google in Mountain View, CA. $174,000 to $252,000 a year. Apply with swipeandhired.
- Research and develop advanced analysis, causal inference analysis of impact, and creative new methodologies (e.g., LLM-based methods) to identify product opportunities from various data sources and product headroom analysis for key AI features.
- Frame and solve ambiguous business problems and communicate data-driven solutions effectively to stakeholders.
- Initiate projects across the organization and drive them to completion autonomously while advocating for users and focusing on long-term responsible growth.
- Interact cross-functionally as a thought partner to influence a wide range of product stakeholders and senior product leadership.
- Leverage AI fluency and tools to drive advanced statistical and machine learning solutions for complex business challenges.
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.
- Experience with Machine Learning algorithms and statistical modeling.
- Experience with statistical data analysis, experimental design (e.g., A/B testing), and causal inference.
- Experience applying causal inference techniques to estimate treatment effects or perform counterfactual analysis.
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 scientific methodologies and Large Language Models
- Experience performing product growth opportunity and headroom analysis.
- Experience solving unstructured business problems and translating results into product recommendations.
- Experience translating unstructured business problems into data-driven product recommendations
- Ability to articulate scientific concepts to both technical and non-technical stakeholders.
Google Search is the world's most trusted and knowledgeable health companion, activating seamless care-paths for every Health Journey. Health is one of Alphabet's top priorities, and this is a high-visibility role focused on evaluations, metrics, and user insights for AI answers in Search Health. You will have significant opportunities to influence product and engineering directions by creating and applying advanced statistical, Machine Learning (ML), and Large Language Model (LLM) methodologies to solve complex business problems.In Google Search, we're reimagining what it means to search for information – any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google .
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 .
- 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.
- Experience with Machine Learning algorithms and statistical modeling.
- Experience with statistical data analysis, experimental design (e.g., A/B testing), and causal inference.
- Experience applying causal inference techniques to estimate treatment effects or perform counterfactual analysis.