Resume

HSBC Hyderabad, India

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Developed and implemented an AI-powered chatbot that assisted HSBC Premier clients with account management,
reducing average response times by 30% and improving customer satisfaction scores by 25%.
● Utilized Python, TensorFlow, and Natural Language Toolkit (NLTK) to improve query routing accuracy, leading to a 20%
reduction in call center volume.
● Optimized large-scale data pipelines using machine learning algorithms such as Decision Trees, Logistic Regression, and
Support Vector Machines (SVM) to enhance fraud detection and risk assessment. This improved detection accuracy by 15%
and minimized false positives.
● Streamlined operational processes in data centers by automating key workflows using Python, SQL, and batch processing
scripts, reducing system downtime by 20%.
● Designed and implemented real-time dashboards in Power BI for monitoring KPIs and SLAs, improving SLA adherence by
20%.
● Built machine learning models to predict client behaviors and optimize financial product recommendations, resulting in a
20% increase in client engagement and product uptake.
● Implemented models using Scikit-learn and traditional algorithms like K-Means clustering, Random Forest, and Logistic
Regression.
● Applied Natural Language Processing (NLP) techniques for document classification and data extraction, using NLTK and
early versions of spaCy to automate client document processing, reducing manual work by 40% and improving accuracy.
● Engineered and implemented machine learning algorithms (Logistic Regression, Decision Trees, K-Nearest Neighbors) to
assess credit risk, and increase approval efficiency by 10%.
● Utilized on-premises infrastructure and private cloud solutions to maintain and scale data processing pipelines, ensuring
system reliability and data security while meeting compliance standards.