Identify abuse patterns Design purpose-built data sets Develop models that prevent harmful users and enable good senders Investigate root-causes of anomalies and gang attacks Analyze signal effectiveness (precision, recall, drift, overlap) Consult with data engineering to shape data pipelines Understand email abuse vectors and trends Build dashboards and alerts for real-time visibility Build machine learning models to prevent abuse at scale with zero false-positive budget