Applied Mathematics · Data Science · AI Engineering
Building machine learning and AI systems from mathematical reasoning, careful validation and practical decision-making.
I'm a Data Scientist and AI Engineer with a B.Sc. and M.Sc. in Mathematics from UNESP. My work spans predictive modeling, statistical analysis and document-based AI, with an emphasis on reproducible experiments, thoughtful validation and understanding how models behave beyond a single score.
Credit-risk modeling with probability quality at the center. Logistic Regression, LightGBM and XGBoost compared on a frozen holdout, with log loss, Brier score, calibration diagnostics and paired-bootstrap uncertainty estimates. The broader decision platform is being built incrementally.
61.5k frozen holdout · ROC-AUC 0.762 · ECE 0.0022
Python · scikit-learn · LightGBM · XGBoost · pytest
Repository → · Model comparison report →
Finding relevant evidence in institutional documents. An independent prototype for semantic search over SEST SENAT documents, using multilingual embeddings, source references and retrieval evaluation, with an optional LLM layer.
Semantic retrieval · source grounding · optional LLM layer
Python · Sentence Transformers · ChromaDB · Streamlit
Estimating late-payment risk from customer and billing data. A supervised-learning case study covering data integration, feature engineering, preprocessing pipelines and comparison of probabilistic classifiers using ROC-AUC and log loss.
Data integration · feature engineering · probabilistic classifiers
pandas · scikit-learn · Random Forest · XGBoost
I also build under Impossible G, focused on local-first, self-hosted AI infrastructure. Its projects package open models as usable services for embeddings, OCR, voice and inference through familiar application interfaces.
Core tools
Python scikit-learn LightGBM XGBoost FastAPI LangGraph
Modeling & statistics
Probabilistic classification · calibration · validation design · experiment tracking
Retrieval & LLM applications
Embeddings · semantic search · reranking logic · source-grounded answers
Applications & engineering
APIs · Streamlit apps · Git workflows · testing with pytest
B.Sc. (2022) and M.Sc. (2024) in Mathematics — UNESP, Brazil.
My graduate work focused on functional analysis and operator theory. In applied work, I bring that attention to assumptions, mathematical structure and the limits of what the data can support.
I also maintain a Mathematics Self-Study Roadmap: a bilingual, book-based path from mathematical foundations to advanced undergraduate and graduate-level topics.
Start with a baseline. Establish a reproducible reference before adding complexity.
Validate the right question. Match evaluation to the prediction setting and the decision the model is meant to support.
Make the evidence inspectable. Keep assumptions, experiments, limitations and results documented alongside the code.
Open to Data Science and AI Engineering opportunities and technical collaborations.
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