• Created revenue models capable of predicting revenue for any subset of users with < 10% error on majority of users and < 5% error on users on high revenue/traffic app using Scala, Python, and Spark

• Expanded and refactored app categorization to remove legacy system and increase coverage in ad serving

• Analyzed and evaluated the impact of new ad serving systems on multi-billion dollar ad revenue stream

• Completed Kaggle Competition on Mobile Data to demonstrate real world application of machine learning

• Implemented models like Linear Regression, Convolutional/Recurrent Neural Network using only Numpy/Scipy

• Designed 10+ tutorials on machine learning models like SVM, Linear Regression, Gaussian Mixture Model

• Developed full stack implementation of attachments on ServiceNow platform using JavaScript, Jelly, and Java

• Wrote script to safety convert all of ServiceNow clients’ databases to handle attachments

• Enabled the ability to do cross database queries using Ruby and Puppet

• Created scripts for CouchDB database interaction to stress test AWS server and couchDB databases

• Built voice bio metrics lockscreen for Android

• Created an audio mixer tool to create training examples for speech processing machine learning algorithms