• Design and implement systems that combine rules, models, feature engineering, and business and product inputs into an email detection product, with senior engineer guidance. • Understand features that distinguish safe emails from email attacks, and how our model stack enables us to catch them. • Identify and recommend new features groups or ML model approaches that can significantly improve detection efficacy for a product. Work with infrastructure & systems engineers to productionize signals to feed into the detection system. • Writes code with testability, readability, edge cases, and errors in mind. • Train models on well-defined datasets to improve model efficacy on specialized attacks • Actively monitor and improve FN rates and efficacy rates for our message detection product attack categories, through feature engineering, rules and ML modeling. • Analyze FN and FP datasets to categorize capability gaps and recommend short term feature and rule ideas to improve our detection efficacy. • Contribute in other areas of the stack: building and debugging data pipelines, or presenting results back to customers in our tools when the occasion arises