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Product Data Scientist, Payments Platform Experience
GoogleSingapore5+ years
Apply 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.
About the job
The Payments team builds and operates Google’s monetization infrastructure that enables all Google products to monetize. This monetization engine moves across countries and supports various business models including B2B, B2C, subscriptions, and marketplaces.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.
Responsibilities
- 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.
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