Google is looking to solve the problem of invalid traffic detection, fraudsters or abuse in its advertising platform by improving and optimizing automated machine learning systems.
Requirements
Coding (e.g., Python, R, SQL)
Querying databases or statistical analysis
Experience with large datasets and big data technologies (e.g., BigQuery, Hadoop, Spark)
Experience in fraud detection, spam mitigation, or anomaly detection within the online advertising ecosystem
Statistical data analysis such as linear models, multivariate analysis, stochastic models and sampling methods
Experience using analytics to solve product or business problems
PhD degree in a related quantitative field
Responsibilities
Support the improvement and optimization of automated machine learning systems for invalid traffic detection, fraudsters or abuse.
Contribute to advanced research to uncover new sources of fraud and develop innovative prevention solutions.
Convey data analysis findings to both technical and non-technical audiences.
Work effectively with various teams of data scientists, engineers, and researchers.
Build trust in the digital ads ecosystem and support the healthy growth of Google’s ads business.
Prevent threats by enabling precise measurement and ensuring the focus remains on the problems through close partnerships across APaS.
Develop unbiased frameworks to measure business health and deliver impact assessments across key areas like risk, business, and user trust.
Other
Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
3 years of work experience using analytics to solve product or business problems
Travel to Mountain View, CA, USA or Los Angeles, CA, USA may be required
Must be eligible to work in the United States
Preferred location: Mountain View, CA, USA or Los Angeles, CA, USA
