• Leveraged LLMs to develop customized natural language processing (NLP) solutions, resulting in a 40% reduction in data analysis time and improved accuracy in extracting key information.

• Implemented t-SNE and PCA on complex chemical datasets, resulting in a 30% reduction in feature space while retaining 95% of the variance, enabling faster and more efficient data processing.

• Developed an intuitive data visualization tool using React, Python RESTful APIs that allowed non-technical stakeholders to interact with t-SNE and PCA outputs, leading to a more informed decision-making process.

• Conducted statistical analysis on clinical data, pinpointing factors for patient scheduling success and optimizing resource allocation.

• Compiled and mined various data sources, leading team efforts to enhance predictive model accuracy by 20%.

• Developed and maintained interactive Spotfire dashboards that visualized critical business metrics for executive decision-making, resulting in an average weekly time savings of 2 hours.

• Enhanced SQL queries, increasing productivity by 40% for faster data retrieval and heightened analysis efficiency.

• Collaborated to design & implement microservices-based pipeline, cutting ETL time by 30% and enhancing data accuracy.

• Led the optimization of data storage, reducing data processing time by 25% & enhancing operational efficiency.

• Developed weather-based load forecasting models using neural networks, achieving 87% accuracy, and optimizing energy planning.

• Managed huge datasets with MongoDB (NoSQL), improving query performance by 40% for efficient data processing.

• Implemented "human in the loop" approach, reducing forecasting errors by 15% and enhancing decision-making.

• Designed robust APIs for model integration, resulting in a 20% improvement in scalability & providing user-friendly access to system.

• Automated model training, deployment using Apache Airflow, and collaborated cross-functionally to deliver impactful data solutions.