• Increased data processing efficiency by 80% using LLM-powered automated data cleaning and preprocessing pipelines.• Developed and deployed a generative AI model that improved customer engagement metrics by 25% through personalized content generation.• Implemented XGBoost for advanced statistical modeling in Python, resulting in highly accurate predictions that empowered improved decision-making. • Conducted in-depth analysis of customer spending patterns, identifying anomalies that indicate potential fraud with a 30% increase in fraud detection rate.• Implemented NLP techniques to analyze 1 million customer feedback records, identifying sentiment trends and improving customer satisfaction by 12%.• Implemented customer segmentation models to identify high-value auto insurance customers with a 15% uplift in customer lifetime value (CLTV).• Led data analysis and predictive modeling initiatives using Python libraries (Scikit-learn, NumPy, pandas) and Spark (MLlib, PySpark), developed various advanced customer segmentation and behavior analysis algorithms, resulting in a 25% increase in customer lifetime value prediction accuracy.

• Engineered custom PyTorch Data Loaders to handle complex data formats increasing data ingestion efficiency by 40% and reducing preprocessing time.• Used AWS EMR to create high-performance machine learning workflows, enabling efficient processing of massive training data and seamless model deployment.• Utilized machine learning algorithms and advanced statistical analysis (decision trees, regression models, SVM, clustering) with the scikit-learn package in Python.• Employed Scikit-learn's extensive machine-learning library to build, train, and evaluate various models, accelerating the development process by 20%.• Preprocessed and cleaned text documents using NLP techniques like tokenization, lemmatization, and stop word removal.• Boosted SQL queries and adjusted resource allocation, improving query performance and reducing overall processing time by 20%, leading to faster data analysis and better decision-making for the organization.• Collaborated with cross-functional teams to implement automated dashboards and reporting using Tableau, empowering stakeholders with data-driven insights, and a remarkable 25% improvement in workforce performance.• Enhanced time-series prediction accuracy by 80% through the implementation of Long Short-Term Memory (LSTM) networks.