TL for Michelangelo - Uber Machine Learning platform.

• Hand-on chief architect for an automated price matching system SavingsCatcher that leverages no-sql technologies such as HBase and Cassandra. The project improves store sales and increases Walmart.com traffic & conversion accounting for 9-figure revenue in 2014 and 2015.

• Created machine learning models and processes for detecting potential fraudulent activities that reduced cost by millions of dollars.

• Developed leader-board system and low-price item suggestion system to increase customer engagement.

• Implemented product grouping system by text mining descriptions and product categories.

• Evaluated new Hadoop technologies. Served on internal Hadoop advisory board.

• Filed five patents.

• Developed machine learning models including a query-result relevance model with high predictive power on click-through rates (using algorithms like conditional inference tree, boosting, and logistic regression), an advertiser conversion model and an ad click-through predictive model for SEM traffic (using decision trees), a real-time page quality scoring system, a low quality merchants detection algorithm, an ensemble method for advertisement classification (using ensemble method that uses SVM, NN, etc. & tools like VW), a Solr query rewrite system for improving relevance, price conversion predictive models, etc. Wrote a query recommendation system and a category suggestion system (using collaborative filtering approach).

• Developed a keyword bidding system that improved monetization and conversion.

• Chief architect for a scalable event-queue driven keyword scoring platform for multiple countries on a 128-node Hadoop cluster. It improved throughput over previous system by at least 10 times.

• Designed and implemented data mining pipeline and search quality monitoring system.

• Served as the chief architect for a new consumer centric offer platform. Was able to deliver the beta release four months after the initial design.

• • Developed new algorithms for managing offer quality and advertisers spending.

• Written a new serving system that improved the average response time to less than 30ms, at least 130 times better than the existing products.

Chief architect for the Domains group.

• Led a team to implement new domain product that went production ahead of schedule. The annual revenue already exceeded eight million dollars.

• Developed patent pending technologies for navigation and advertiser bid discounting that improved monetization for publishers and decreased cost for advertisers.

• Developed a domain traffic quality prediction algorithm using various machine learning techniques.

• Designed a statistical feedback model for pricing publishers’ traffic that achieved the best overall channel quality.

• Implemented a search-query to semantic concept mapping system that increased query coverage.

• Designed and implemented a behavior targeting platform for sharing users information across multiple publishers and ad serving products.

• Developed an algorithm for detecting click spam traffic.

• Worked with business unit on project priority and on developing unique competitive features.

• Coordinated with other groups on resource allocation and capacity planning.