Developed and deployed scalable data pipelines for LLM protocols using AWS. Ensured data quality and model performance through rigorous monitoring and evaluation. Accelerated customer service response times from 20 minutes to an average of 120 seconds by leveraging LLM-powered knowledge bases to proactively detect and address customer inquiries. Automated the process of error log analysis using NLP models (Naive Bayes, DBSCAN) built with Scikit-learn. This enabled efficient detection and classification of errors, contributing to improved system security. Leveraged machine learning and time series models to accurately forecast demand, resulting in optimized shipping operations and reduced shipping costs by 10%.

Developed and deployed a scalable data pipeline to ingest and process demographic, traffic, and competitor data, enabling the training and deployment of a Random Forest model for brand affiliation prediction. This involved building ETL processes, data validation checks, and integrating the model into a production environment. Engineered the data infrastructure and automated pipelines that power a nationwide fuel distribution planning system. This included designing and implementing the data lake, ETL processes, and API integrations necessary to support the AI-powered optimization engine and ensure data availability and reliability for real-time planning. Built and maintained the data infrastructure to support hyper-segmentation modeling. This involved designing and implementing the data warehouse, ETL processes for customer data, and the real-time data feeds that enable personalized offer delivery through various channels. Focused on data quality, consistency, and efficient access for the data science team. Developed and implemented the data architecture and automated data pipelines to support automated monthly revenue calculations. This included building data quality checks and validation rules to ensure accuracy and reduce invoice errors by 90%. Focused on the reliability and performance of the data pipelines feeding the calculation models.