Hybrid Data Science Systems for Advanced E-Commerce | Hybrid Models for Personalization | AGPH Books
by Krishna Chaitanya Yarlagadda
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Book Title: Hybrid Data Science Systems for Advanced E-Commerce | Hybrid Models for Personalization | AGPH Books
Hybrid Data Science Systems for Advanced E-Commerce explores how modern e-commerce platforms have evolved into intelligent, data-driven ecosystems. Today, online retail relies on advanced analytics to understand customers, optimize operations, and drive growth. As a result, hybrid data science systems have become essential for competitive advantage. This book explains how hybrid frameworks combine classical machine learning with advanced AI techniques. By doing so, businesses can extract deeper insights while maintaining scalability and interpretability. The book demonstrates how these systems solve real-world e-commerce challenges in practical and effective ways.
First, readers learn how hybrid models power recommendation engines and customer segmentation. Next, the book explores predictive analytics and dynamic pricing strategies. These approaches enable adaptive, data-driven decision-making at scale. Importantly, each concept is explained with clarity and real-world relevance. In addition, the book emphasizes interpretability and governance. It explains why understanding model behavior matters as much as performance. Therefore, readers gain confidence in applying hybrid systems responsibly in production environments.
Beyond core algorithms, the book examines emerging trends shaping the future of e-commerce. These include immersive personalization, augmented reality integration, adaptive learning systems, and continuous feedback loops. Together, these innovations redefine how customers interact with digital platforms.
Overall, this book blends theory, case studies, and actionable strategies. It is designed for researchers, data scientists, and industry professionals. Ultimately, it equips readers to build intelligent, scalable, and future-ready e-commerce systems using hybrid data science approaches.
About the Author
Krishna Chaitanya Yarlagadda is a seasoned Data Science and AI leader with over thirteen years of experience building intelligent systems across financial services, e-commerce, and digital platforms. Throughout his career, he has delivered solutions that improve decision intelligence, model governance, and enterprise analytics.
Currently, Krishna serves as Director of Data Science & AI in Model Risk Management at Atlanticus. In this role, he leads the development, validation, and governance of AI and machine learning models.Therefore these models support credit risk, fraud prevention, and portfolio management. Moreover, he oversees model lifecycle modernization. His work integrates explainable AI, stress testing, continuous monitoring, and regulatory compliance into unified AI frameworks. As a result, organizations achieve stronger governance and regulatory alignment.
Previously, Krishna worked as Senior Manager of Data Science, Analytics & Reporting at Mercury Financial. There, he led machine learning initiatives for credit-card collections and customer engagement. He built payment likelihood models, segmentation systems, and AI-driven personalization frameworks.
Consequently, these solutions improved payment conversions, reduced charge-off losses, and enhanced operational efficiency. His experience bridges advanced analytics with real-world business impact.
About The Publisher:
AGPH Books is a Professional Self Book Publishing House based in Central India, specializing in academic, professional, fiction, and non-fiction books in both print, digital and audio formats. The publishing house produces textbooks, research and reference works, biographies, self-help titles, children’s books, literary fiction, poetry, and general interest publications. With a transparent publishing process and strong digital distribution, AGPH Books ensures global availability through Google Books, Amazon, Flipkart, and its official website store, supporting authors and institutions in reaching a wide and diverse readership.
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About the Author
Krishna Chaitanya Yarlagadda is an accomplished Data Science and AI Leader with more than thirteen years of experience designing and operationalizing intelligent systems that power decision intelligence across financial services, e-commerce, and digital markets. His expertise lies at the intersection of machine learning, predictive analytics, risk modeling, and enterprise-scale data engineering, where he has consistently delivered solutions that transform business performance, enhance model governance, and enable organizations to innovate responsibly. Krishna currently serves as Director of Data Science & AI in Model Risk Management at Atlanticus, where he leads the development, validation, and governance of AI/ML models used across credit risk, fraud prevention, and portfolio management. In this role, he oversees model lifecycle modernization, ensuring that advanced techniques—such as explainable AI (XAI), stress testing, model monitoring, and regulatory compliance frameworks—are integrated into a unified, responsible AI infrastructure. His leadership helps safeguard model performance, strengthen governance standards, and align complex analytical ecosystems with federal regulatory expectations. Before joining Atlanticus, Krishna served as Senior Manager of Data Science, Analytics & Reporting at Mercury Financial, where he led digital analytics and machine learning transformation for credit-card collections and customer engagement. He built and deployed payment likelihood models, customer segmentation systems, and AI-driven personalization frameworks that improved digital payment conversions, reduced charge-off losses, and narrowed operational inefficiencies. His contributions included building a real-time analytical layer for collections, accelerating insights, and enabling dynamic outreach strategies that materially improved portfolio performance. Krishna previously worked at Amazon, where he served as a Senior Business Intelligence Engineer for Amazon Private Brands. During his time there, he developed large-scale forecasting, classification, and anomaly detection systems that supported retail planning, selection optimization, and product performance analytics. His work on the ASIN Override Risk Model, ASIN Driver Dashboard, and automated data quality pipelines significantly reduced manual workload, improved forecast accuracy, and delivered measurable savings in compute resources—demonstrating his ability to combine technical rigor with operational efficiency. Earlier in his career, Krishna spent six years at JPMorgan Chase, leading high-impact modeling initiatives within recovery, segmentation, and fraud risk domains. He developed multiple machine learning models—including the Recovery Score Model, Real-Time Sentiment Engine, and Risk Segmentation Systems—that directly influenced strategy, portfolio performance, and customer experience. His models improved liquidation outcomes, enhanced targeting precision, and created measurable financial value for one of the world’s largest financial institutions. Krishna holds a Master’s degree in Analytics and Data Science from Oklahoma State University. He has authored research on AI-driven optimization, predictive modeling, and data-driven decision systems, and contributes actively as a peer reviewer, session chair, and conference judge for leading AI and computer science forums. His certifications span AWS Machine Learning Specialty, Tableau Data Scientist, Azure Fundamentals, SAS Advanced Programming, and numerous analytics and cloud technologies. With a rare combination of technical depth, strategic leadership, and cross-industry expertise, Krishna Chaitanya Yarlagadda continues to advance the frontier of AI-powered decision intelligence. His work embodies the future of digital markets—where scalable analytics, responsible AI, real-time intelligence, and trustworthy automation redefine how enterprises operate, compete, and innovate.
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