About

Anurag Pandey is an experienced Computer Scientist with a strong background in Machine Learning and Data Engineering. Currently working as an Applied ML Engineer at Adobe, Anurag has a demonstrated history of working in the FinTech and AdTech industry. He holds a Master of Technology in Data Science from the prestigious Indian Institute of Technology, Hyderabad. Anurag has worked with several top-notch organizations, including Morgan Stanley and American Express. During his tenure at Morgan Stanley, he worked with the E-Trade team to create and onboard an Idea/Offer level Recommendation System using AWS as the underlying platform. He also optimized data processing using Snowflake, AWS Glue, and Sagemaker Processing. Anurag played a key role in standardizing AI/ML best practices across the entire Model Development LifeCycle. At American Express, Anurag led a team of 11 and built a self-serve/assisted/custom Machine Learning model platform that utilized batch feeds and scored/auto-retrained ML Models using state-of-the-art algorithms like XGBoost, LightGBM, and Deep Neural Nets. He also architected solutions to key decisioning models like Consumer Response Models and Merchant Recommendation Models. Anurag's educational background includes a Master of Technology in Data Science from Indian Institute of Technology, Hyderabad, an Advanced Programme in Data Sciences from Indian Institute of Management, Calcutta, and a Bachelor of Engineering in Electronics and Communications Engineering from Panjab University. Anurag is proficient in several technologies, including ML, AWS, Python, Java, Sagemaker, Snowflake, AWS Glue, Spark, SQL, Recommendation system, Deep Learning, Data Scientist, Research Scientist, Big Data, AI, XGBoost, Hadoop, NLP, Kafka, Senior Software Engineer, Computer Vision, Apache Hive, Scikit, MapReduce, MySQL, NoSQL, Elastic Search, ETL, Pytorch, Mobile, and Web. With 7.03 years of relevant experience, Anurag is a strong professional with a passion for leveraging machine learning to solve complex business problems.