Director Machine Learning & Data Science, American Express
Dr. Manish Gupta is an advanced analytics professional with 15 years of experience in building & leading data science & analytics teams for developing competencies in customer analytics, real-time recommendation system, big data and machine learning solutions across various industries such as Internet/E-commerce, Banking, BPO and Defence.
He recently joined American Express as Director, Machine Learning Research to carry out business specific machine learning and deep learning research and to establish machine learning best practice for business. In his most recent role, Manish served as Senior Vice President, Analytics at InfoEdge which is the parent company of various popular portals of India like Naukri.Com, JeevanSathi.Com, 99Acres.Com, Shiksha.Com etc. He built & led data scientist team from scratch to develop scalable personalised real-time recommendation systems using huge amounts of structured and unstructured data using cutting-edge Machine Learning, Data Science, BigData, Deep Learning & Text Mining, Natural Language Processing Technologies. He has also previously worked with Global Decision Management, Citibank as
He has also previously worked with Global Decision Management, Citibank as the analytic lead for leveraging untraditional data sources such as click stream data, voice tag data along with regular financial data for customer acquisition, retention and attrition using BigData and advanced analytics technologies. He has also worked as Head (R&D) at Innovation Labs at 24/7 Customer where he developed patented technologies for chat categorization and web acquisition engines. He has served the country as Scientist at Defence Research and Development Organization (DRDO) a defence research organisation in India. He has received several awards including Scientist of the Year, Technology Award for his contribution to developing state of the analytics solutions for armed forces.
Manish holds a PhD degree from Indian Institute of Technology – Delhi in the area of Machine Learning with more than 15 research publications in leading international journals and conferences with 1 US Patent which has more than 130 citations.
Patent descriptionChat categorization uses semi-supervised clustering to provide Voice of the Customer (VOC) analytics over unstructured data via a historical understanding of topic categories discussed to derive an automated methodology of topic categorization for new data; application of semi-supervised clustering (SSC) for VOC analytics; generation of seed data for SSC; and a voting algorithm for use in the absence of domain knowledge/manual tagged data. Customer service interactions are mined and quality of these interactions is measured by “Customer’s Vote” which, in turn, is determined by the customer’s experience during the interaction and the quality of customer issue resolution. Key features of the interaction that drive a positive experience and resolution are automatically learned via machine learning driven algorithms based on historical data. This, in turn, is used to coach/teach the system/service representative on future interactions.