Product Data Scientist, Payments Platform Experience
The Identity and Risk teams safeguard Google’s platform by developing solutions to mitigate fraud, abuse, and identity threats. The Identity team focuses on high-assurance verification and frictionless user onboarding, while the Risk team builds infrastructure necessary to manage financial risk and prevent wide-scale platform abuse. Together, they enable trusted global commerce by balancing platform protection with seamless experiences.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $138000 - $198000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
- Perform analysis by utilizing relevant tools (e.g., SQL, R, Python). Using comprehensive technical knowledge, use custom data infrastructure or existing data models.
- Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python). Format, re-structure, and validate data to ensure quality.
- Report Key Performance Indicators (KPIs) to support business reviews with cross-functional/organizational leadership team. Translate analysis results to business insights or product improvement opportunities.
- Provide analytical insights and recommendations to influence product feature development decisions, and with some guidance.
- Build and prototype analysis and business cases iteratively to provide insights at scale. Develop comprehensive knowledge of Google data structures and metrics.
Minimum qualifications:
- Bachelor's degree in statistics, mathematics, data science, engineering, physics, economics, a related quantitative field, or equivalent practical experience.
- 5 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL) or 2 years work experience with a Master's degree.
Preferred qualifications:
- Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
- Experience working on statistical/casual inference techniques across experimentation and observational studies.
- Experience working in the payments, online ecommerce, or marketplace industry.