I’m a junior at Baruch College, pursuing a BBA in Statistics and Quantitative Modeling, Data Science Track. I use statistics and programming to explore financial data, risk, and mathematical decision-making.

Seeking internship opportunities in data analytics, data science, and fintech.

Evaluated six energy ETF pairs using 2021–2023 training data applying mean-reversion strategies through cointegration testing, OLS hedge-ratio estimation, and rolling Z-score signals. Backtested FENY–VDE using a fixed training-period hedge ratio on 2024–2025 historical data to ultimately calculate approximately 0.8% cumulative return before trading and borrowing costs.

Exploring fraud detection in a highly imbalanced transaction dataset using SMOTE and a Random Forest classifier. Increased precision from 88% to 92% while recall decreased from 83% to 82% at a 75% classification threshold, examining the tradeoff between precision and recall.

My progress through Angela Yu’s bootcamp, with exercises and projects covering control flow, functions, data structures, debugging, and introductory object-oriented programming.

• Statistical analysis: regression, hypothesis testing, cointegration, statsmodels
• Machine learning: classification, imbalanced data, precision–recall evaluation
• Visualization & development: Matplotlib, Jupyter, PyCharm, Git and GitHub

Making my projects easier to read, strengthening model evaluation, and communicating results and limitations clearly. My next steps include evaluating trading costs and making each project smoother to run from a fresh environment.

I’d welcome conversations about internships, research, and projects involving financial data or applied statistics.