Published inLyft EngineeringHow Lyft designs the Machine Learning Software Engineering interviewIterations on revealing recurring patterns of thought, feeling, and behaviorOct 22, 2019Oct 22, 2019
Published inLyft EngineeringFingerprinting fraudulent behaviorUsing neural networks to gobble up the trail of breadcrumbs left by fraudstersJul 24, 20183Jul 24, 20183
Published inLyft EngineeringFrom shallow to deep learning in fraudA Research Scientist’s journey through hand-coded regressors, pickled trees, and attentive neural networksJul 9, 20184Jul 9, 20184
Published inLyft EngineeringInteractions in fraud experiments: A case study in multivariable testingA while ago we observed something curious when we ran a set of simultaneous A/B tests around multiple antifraud features. These tests were…Oct 5, 20171Oct 5, 20171
