I’m a passionate explorer of data-driven insights, blending scientific reasoning with innovative problem-solving. As a graduate student in the MS Data Science program at the University of Michigan, I specialize in unraveling intricate challenges at the nexus of data, technology, and strategy. My journey spans diverse industries, including over a year in Luxury Retail as a Data Analyst for the MUSE Customer Loyalty app, where I transformed customer behavior data into strategic insights that drove loyalty and engagement. During my time at GKN Automotive, I developed a cutting-edge Machine Learning model that improved testing accuracy by reducing error rates by 10%, unlocking critical insights to optimize product performance. With expertise in Machine Learning, Deep Learning, AI, Data Analysis, and dashboarding, I command a robust and diverse tech stack. I’m proficient in SQL, Python, R, C++, and Java, and navigate the world of Big Data using Hadoop and Spark. My data storytelling takes shape through Looker, Tableau, and Power BI, while AWS QuickSight and Google Cloud’s BigQuery enable me to harness the full potential of cloud infrastructure. I thrive on pushing boundaries, using tools like Microsoft Office Suite and GitHub to turn complex datasets into symphonies of insight and action. My pursuit? To continuously craft transformative solutions that not only answer questions but uncover opportunities for innovation and growth