Gurpreet Singh
About
Gurpreet Singh is from New York, New York, United States. Gurpreet works in the following industries: "Marketing and Advertising". Gurpreet is currently Lead Data Scientist at AdTheorent, located in New York, New York, United States. In Gurpreet's previous role as a Senior Data Scientist at AdTheorent, Gurpreet worked in Greater New York City Area until Feb 2021. Prior to joining AdTheorent, Gurpreet was a Data Scientist at AdTheorent and held the position of Data Scientist at New York.
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Gurpreet Singh's current jobs
Gurpreet Singh's past jobs
Delivered solutions for various Data Science projects ranging from research & development to creation of production level machine learning models for personalized ads targeting working with very large datasets. • Inspired & directed the feature engineering efforts and led the model building on top of these features that allow for much more precise & intelligent predictions. • Designed and Implemented Real Time Decision Machine on Real Time Bidding (RTB) platform for scoring 25 billion+ ad requests every day to predict the likelihood of User Engagement using incremental classification models (Java, Deployed on AWS) • Designed data schemas used for Experimentation, Retargeting, Reporting and ad-hoc analysis. • Worked on several interesting and critical data analytic challenges related to Data Quality & Validation, Ads Fraud, Attribution & Measurement that contributed to the overall reliability and performance of core targeting algorithms. Used SQL, Python (pandas) and Apache Spark for doing most of the analytics work. • Collaborated with different business teams to develop strategy, metrics & KPIs for campaign performance and classification models. • Participation in creating optimal Real Time Bidding (RTB) strategies to optimize the campaign performance and yield results for Advertisers. • Reduced New User Acquisition costs for Top Brands by 10% to 80% by developing robust Classification models using data from various sources. AdTheorent is a technology company that is transforming mobile advertising through data-driven predictive solutions. AdTheorent's machine learning technology - the Real Time Learning Machine™ (RTLM) - combines millions of data attributes into precise and accurate adaptive models for the purpose of identifying optimal audiences.
Delivered solutions for various Data Science projects ranging from research & development to creation of production level machine learning models for personalized ads targeting working with very large datasets. • Inspired & directed the feature engineering efforts and led the model building on top of these features that allow for much more precise & intelligent predictions. • Designed and Implemented Real Time Decision Machine on Real Time Bidding (RTB) platform for scoring 25 billion+ ad requests every day to predict the likelihood of User Engagement using incremental classification models (Java, Deployed on AWS) • Designed data schemas used for Experimentation, Retargeting, Reporting and ad-hoc analysis. • Worked on several interesting and critical data analytic challenges related to Data Quality & Validation, Ads Fraud, Attribution & Measurement that contributed to the overall reliability and performance of core targeting algorithms. Used SQL, Python (pandas) and Apache Spark for doing most of the analytics work. • Collaborated with different business teams to develop strategy, metrics & KPIs for campaign performance and classification models. • Participation in creating optimal Real Time Bidding (RTB) strategies to optimize the campaign performance and yield results for Advertisers. • Reduced New User Acquisition costs for Top Brands by 10% to 80% by developing robust Classification models using data from various sources. AdTheorent is a technology company that is transforming mobile advertising through data-driven predictive solutions. AdTheorent's machine learning technology - the Real Time Learning Machine™ (RTLM) - combines millions of data attributes into precise and accurate adaptive models for the purpose of identifying optimal audiences.