New York City Metropolitan Area
I’ve always been drawn to the challenge of making sense of complex information. For me, data science feels like working on a 𝐟𝐚𝐬𝐜𝐢𝐧𝐚𝐭𝐢𝐧𝐠 𝐩𝐮𝐳𝐳𝐥𝐞-𝐞𝐚𝐜𝐡 𝐝𝐚𝐭𝐚𝐬𝐞𝐭 𝐨𝐟𝐟𝐞𝐫𝐬 𝐚 𝐧𝐞𝐰 𝐦𝐲𝐬𝐭𝐞𝐫𝐲 𝐭𝐨 𝐮𝐧𝐫𝐚𝐯𝐞𝐥, and I’m motivated by the 𝐜𝐮𝐫𝐢𝐨𝐬𝐢𝐭𝐲 𝐭𝐨 𝐟𝐢𝐧𝐝 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐚𝐧𝐝 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐭𝐡𝐚𝐭 𝐚𝐫𝐞𝐧’𝐭 𝐢𝐦𝐦𝐞𝐝𝐢𝐚𝐭𝐞𝐥𝐲 𝐨𝐛𝐯𝐢𝐨𝐮𝐬. This drive to solve problems is at the heart of why I want to be a data scientist. With 1.5 years of hands-on experience at 𝐀𝐦𝐞𝐫𝐢𝐜𝐚𝐧 𝐄𝐱𝐩𝐫𝐞𝐬𝐬, I’ve had the opportunity to dive deep into 𝐜𝐫𝐞𝐝𝐢𝐭 𝐫𝐢𝐬𝐤 𝐝𝐚𝐭𝐚 𝐬𝐜𝐢𝐞𝐧𝐜𝐞-solving real business challenges and collaborating with talented teams to develop and implement robust credit risk models. My work there helped inform smarter business decisions and improve risk management, and it’s where 𝐈 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐞𝐝 𝐣𝐮𝐬𝐭 𝐡𝐨𝐰 𝐦𝐮𝐜𝐡 𝐈 𝐞𝐧𝐣𝐨𝐲 𝐭𝐮𝐫𝐧𝐢𝐧𝐠 𝐜𝐨𝐦𝐩𝐥𝐞𝐱 𝐝𝐚𝐭𝐚 𝐢𝐧𝐭𝐨 𝐚𝐜𝐭𝐢𝐨𝐧𝐚𝐛𝐥𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬. Currently, I’m wrapping up my 𝐌𝐚𝐬𝐭𝐞𝐫 𝐨𝐟 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 at the University of Maryland in December 2025, where I’ve maintained a 𝟑.𝟗𝟓/𝟒.𝟎 𝐆𝐏𝐀 𝐰𝐡𝐢𝐥𝐞 𝐬𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐢𝐧𝐠 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞. My journey so far has given me a strong technical foundation in 𝐏𝐲𝐭𝐡𝐨𝐧, 𝐒𝐐𝐋, 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐚𝐧𝐝 𝐆𝐞𝐧 𝐀𝐈, but more importantly, it’s fueled my passion for uncovering stories and solutions hidden within data. I thrive in fast-paced, collaborative environments and love the challenge of building scalable solutions that make a tangible impact. I’m 𝐞𝐱𝐜𝐢𝐭𝐞𝐝 𝐭𝐨 𝐛𝐫𝐢𝐧𝐠 𝐦𝐲 𝐜𝐮𝐫𝐢𝐨𝐬𝐢𝐭𝐲, 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐚𝐥 𝐦𝐢𝐧𝐝𝐬𝐞𝐭, 𝐚𝐧𝐝 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐬𝐞𝐧𝐬𝐞 𝐭𝐨 𝐚 𝐝𝐚𝐭𝐚 𝐬𝐜𝐢𝐞𝐧𝐜𝐞 𝐭𝐞𝐚𝐦 that values innovation and real-world results
BUDT 704: Data Processing & Analytics in Python Mentored students on hands-on analytics and machine learning projects using Pandas, Statsmodels, and Scikit-learn, covering data wrangling, exploratory analysis, regression and classification, cross-validation and model evaluation, and model interpretability using feature importance and Shapley value–based methods
Designed and automated monthly ETL pipelines and Tableau dashboards tracking key risk metrics across a $20 B credit portfolio with 100 % data accuracy Performed large-scale data mining and risk-pattern detection using Python, SQL, and Tableau to identify emerging high-risk segments Designed and executed backtesting and experiment-style evaluations to measure the causal impact of risk and policy changes, informing data-driven strategy decisions Proficient in SQL, Python, Pyspark, Tableau, Microsoft Excel, and SAS