Research Data Scientist, Ads Safety and Spam

Minimum qualifications:

• 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, coding (e.g., Python, R, SQL), querying databases or statistical analysis or a PhD degree.

Preferred qualifications:

• 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 a PhD degree.
• 4 years of experience in data analysis, including statistical data analysis such as linear models, multivariate analysis, stochastic models and sampling methods.
• 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.

About the job

As a Data Scientist on the Ads Privacy and Safety (APaS) Data Science team, you will leverage your expertise to provide data insights that directly inform important decisions and product enhancements. Your work will be crucial in protecting users and advertisers from fraud, invalid activity and ensuring a fairly compensated, clean and trustworthy ad platform. You will work on important problems across Ads Spam and Ads Safety to build trust in the digital ads ecosystem and support the healthy growth of Google’s ads business. Ads Privacy and Safety is dedicated to fostering trust and transparency within the Google Ads ecosystem. The Analytics and Insights team plays a crucial role in safeguarding the integrity of Google's advertising platform. By focusing on data-driven objectivity, accountability, and user-centricity, this team develops unbiased frameworks to measure business health and deliver impact assessments across key areas like risk, business, and user trust. They prevent threats by enabling precise measurement and ensuring the focus remains on the problems through close partnerships across APaS and the team provides continuous measurement and influences important decisions with insights.

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.