I’m a Computer Science student at Indiana University, specializing in Artificial Intelligence and minoring in Business. My professional experience includes an internship with Capgemini on the McDonald’s Account, where I contributed to client-facing deliverables and strategic initiatives. I’m currently building projects that apply data analysis technologies to strengthen my technical and analytical skills.
I’m eager to pursue opportunities in technical data analyst roles, with the long-term goal of moving into client-facing positions where I can bridge the gap between business stakeholders and technology leaders. I’m a fast learner with a strong work ethic and I thrive at the intersection of technology and business.
Machine learning models (Logistic Regression, Decision Tree, Random Forest) applied to telecom customer data to predict churn and identify segment-specific retention drivers.
Python • Scikit-learn • Matplotlib • Pandas • NumPy • Tableau
End-to-end data pipeline showcasing AWS RDS, SQL feature engineering, and machine-learning models to predict late deliveries and customer churn. Raw Olist data was loaded to AWS RDS, queried and tested locally, exported to S3, and then used for analytics and modeling.
Python • PostgreSQL • AWS RDS • AWS S3 • Scikit-learn • XGBoost • Pandas
