Minimum qualifications:
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, a related quantitative field, or equivalent practical experience.
- 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.
- 2 years of work experience in data science or quantitative analytics with focus on statistical modeling, machine learning, and visualization.
- Experience, education/training, or demonstrated interest in machine learning.
- Excellent communication skills and ability to translate analysis results into business recommendations.
- Excellent problem solving skills, with the ability to articulate product questions and pull data from datasets (e.g., SQL, BigQuery or equivalent technologies).
About the job
Chrome is a consumer, developer, and enterprise facing product and platform with users. Data-driven insights and metrics are a key part of making decisions about Chrome.
The Data Science team blends research and product knowledge to address tests. The team leverages knowledge in advanced statistical, machine learning techniques, predictive modeling, evaluation, experimentation, forecasting, exploratory analysis, and more to drive data-informed innovation, unlock opportunities, and enhance user experiences within Chrome.
The mission is to guide Chrome's decisions and innovations through data-informed insights. As a Senior Data Scientist, you'll have a unique opportunity to work across various teams to enhance Chrome's features and understand user needs.
You will contribute to evolving Chrome into a more personalized and helpful user agent. You'll play a vital role in integrating Google's technology and adapting Chrome to meet the needs of users in their homes, workplaces, and more.
Responsibilities
- Leverage advanced statistical methods on datasets to extract insights from events and features across different organizational sources.
- Develop and deploy automated solutions, ranging from SQL query automation to real-time Python classification and ML modeling, to address key tests.
- Analyze intricate product and platform usage patterns, translating data-driven insights into product strategy and engineering decisions.
- Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer, and provide feedback to translate and refine business questions into analysis, evaluation metrics, or mathematical models.
- Use custom data infrastructure or existing data models as appropriate, design and evaluate models to express and solve defined problems.