I am a Data Science Co-op at Dark Matter Technologies. I lead the integration of AIVA's A/B testing pipeline into loan processes with AWS CodeBuild and CodePipeline, boosting deployment speed and efficiency by 12%. I also deploy machine learning models via AWS SageMaker to mimic cognitive thinking, improving data analysis and productivity by 17%. Additionally, I optimize BERT and image processing pipelines with AWS, increasing document verification accuracy and efficiency, and leveraging Power BI for storytelling with business intelligence, complex data analysis, and data visualization. I am pursuing a Master's in Data Science at Drexel University, focusing on cutting-edge technologies and real-world applications. Previously, I worked as a Data Scientist at Digital Pass, where I implemented Python-based sentiment analysis on a 500TB dataset, achieving a 13% improvement in customer sentiment accuracy and insights. I also designed and implemented Azure-based data warehousing solutions, improving data analysis efficiency by 30%, mapped user interactions, and enhanced content dissemination by leveraging graph theory, resulting in an 18% increase in audience engagement and campaign performance metrics. I am a highly motivated Data Science enthusiast with a strong background in Machine Learning, Deep Learning, Natural Language Processing (NLP), and Computer Vision. My expertise extends to Amazon Web Services (AWS), and I am skilled in Python, R, and Tableau, enabling me to derive valuable insights from data and deliver impactful solutions. I am always open to exciting opportunities and collaborations in data science. Let's connect and together unlock the full potential of data-driven innovation!