Rodolfo Lopez

About

Recent McCombs School of Business (UT Austin) Post Graduate Program graduate in Data Science & Business Analytics with hands-on project experience spanning stock clustering/segmentation and hotel booking-cancellation prediction. Strong in end-to-end analysis workflows (EDA, cleaning, feature engineering) and rigorous model comparison/selection, with exposure to boosting methods and imbalanced-data techniques; limited experience so far with embeddings/vector databases and production deployment.

Experience

Education

Key Strengths

• Built clustering analysis (K-means, hierarchical, PCA) to segment stocks and summarize group characteristics
• Developed booking-cancellation prediction model for hotel data; compared logistic regression vs decision tree approaches
• Structured data-cleaning workflow (data profiling, datatype checks, duplicates/missing handling, column pruning)
• Feature engineering with practical transformations (e.g., year established to years-since-established) and appropriate encoding
• Model evaluation discipline using train/validation/test splits and overfitting checks via train-vs-validation metric gaps
• Experience comparing multiple classifiers (AdaBoost, Gradient Boosting, XGBoost) including imbalanced-data strategies

Reference Highlights

• Technical depth in ML/NLP
• Curious; asks the right questions
• Practical approach to real-world case studies/problems
• Strong end-to-end ownership of projects
• Structured approach to building stable, maintainable pipelines
• Focus on scalability and reliability
• Clean, modular, well-documented code
• Builds reproducible workflows
• Efficient data processing (vectorization, optimized SQL, chunking)
• Strong feature engineering
• Effective handling of imbalanced data
• Improved recall for high-risk cases while maintaining accuracy (project outcome)
• Clear communication with technical peers
• Adapts quickly to team standards
• Translates complex concepts into actionable business insights
• Avoids jargon; focuses on business impact
• Data-driven, easy-to-follow decision-making
• Extremely reliable
• Easy to work with
• Eager to learn
• Open to feedback