I'm a Data Scientist with an MSc in Computer Science (UFMG), focused on applied machine learning — particularly in healthcare and risk modeling contexts. My research covers class imbalance, probabilistic calibration, and building pipelines that are not just accurate, but reliable and interpretable.

Currently working as a Data Scientist at a SaaS company while pursuing roles that allow deeper ML work.

End-to-end ML pipeline for predicting injury risk under severe class imbalance. Core contributions include probabilistic calibration evaluation (reliability diagrams, ECE, MCE), PR AUC as primary metric, and a nested cross-validation architecture that avoids data leakage.

Manuscript in preparation for submission to a peer-reviewed sports medicine journal.

Machine Learning & Data scikit-learn · XGBoost · LightGBM · imbalanced-learn · pandas · NumPy · SciPy

Probabilistic & Statistical calibration curves · PR AUC · bootstrap CIs · Markov Blanket feature selection

Engineering & Infra Python 3.11 · SQL · Metabase · Git · VS Code · Jupyter · Azure Devops

Learning / Exploring MLflow · Optuna · survival analysis · LLM fine-tuning (MLX + LoRA)

📬 LinkedIn — best way to reach me for professional conversations.