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Rodolfo Lopez
Senior Math Educator transitioning to Data Science & Business Analytics
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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.
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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
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