Alese Lafonta

Analytical data scientist with a solid foundation in advanced mathematics and experience in Python, R, SQL, machine learning, statistical testing, and multimodal AI evaluation

Houston, HarrisMember since February 17, 2026

PythonSQLEDAFeature EngineeringPredictive ModelingA/B TestingMachine LearningPandasScikit-learnMatplotlibggplot2TableauGitDockerAWSS3Lambda

About

Analytical data scientist with a solid foundation in advanced mathematics and experience in Python, R, SQL, machine learning, statistical testing, and multimodal AI evaluation. Skilled in transforming raw data into actionable insights through EDA, feature engineering, predictive modeling, and data visualization. Adept at validating model performance, documenting findings, and supporting data-driven decision-making across academic, research, and AI evaluation environments.

Experience

AI Trainer – Mathematics Domain - Handshake AI

(2025-11 - 2025-12)

Labeled and evaluated 2K+ multimodal samples (images, audio, video, text) to strengthen AI mathematical reasoning.
Assessed model outputs for accuracy, clarity, and logical consistency, improving evaluation consistency by ~15%.
Provided structured feedback that enhanced explanation quality and pattern recognition across core math topics.
Supported refinement of annotation guidelines, reducing reviewer ambiguity by 20%.

Data Science Projects - Freelance & Independent

(2022-06 - 2025-11)

Built end-to-end analytics workflows using Python, SQL, Pandas, Scikit-learn, and Tableau to explore consumer, health, and retail datasets.
Applied feature engineering, predictive modeling, and A/B testing to evaluate user behavior and optimize decision-making.
Developed and deployed small-scale models using AWS (S3, Lambda) and Docker for reproducible experimentation.
Version-controlled all work using Git/GitHub, improving code organization and collaboration readiness.

Academic Research Assistant - University of Houston

(2021-01 - 2022-05)

Collected and processed 10K+ rows of geological data using R to analyze mineral–soil relationships.
Applied regression and correlation analysis, identifying 3 statistically significant predictors of mineral concentration.
Created 15+ visualizations with Matplotlib and ggplot2 for reports and presentations.