I'm a very communicative scientist who believes that this is a relevant virtue not only for research teams but also for development teams, as communication, for teamwork, is essential. However, I consider curiosity to be my fundamental characteristic and, like a good scientist, the effort within that curiosity 🔭🌱.

I currently work as a Data Scientist II, developing forecasting and credit-model validation solutions for the banking sector, and as a part-time Assistant Professor, teaching Computational Mathematics, Algorithmic Reasoning, and Fundamentals of Computational Systems.

I am passionate about simulating natural phenomena from my small laptop — it's like having a small part of the universe inside my room.

My latest project focused on the development of an open-source scientific package for the fast and accurate simulation of the time-independent wavefunction of a Quantum Harmonic Oscillator, a model widely used in Photonic Quantum Computing.

This package, called Fast Wave, was developed as part of my master's research, which is now completed.

My journey in Artificial Intelligence and Data Science blends research, applied development, banking, and production-ready solutions.

I specialize in:

• ✅ Time-series forecasting for bank budgeting and Expected Credit Loss estimates under BACEN Resolution No. 4,966.
• ✅ Forecasting models such as ARIMA, SARIMAX, Prophet, N-BEATS, N-HiTS, Autoformer, and FEDformer.
• ✅ Credit-model validation, including standardized validation pipelines, quantitative scoring, and governance criteria.
• ✅ Risk Modeling and Validation, including Credit, Operational, Market, Microcredit, and Climate and Environmental Risks.
• ✅ Challenger models using MLP, XGBoost, LightGBM, XGBSE, KMeans, PAM, and CLARA.
• ✅ Probabilistic and financial models such as Value at Risk (VaR) and RAROC.
• ✅ Anomaly detection for event logs using Isolation Forest, RabbitMQ, and Elasticsearch.
• ✅ Natural Language Processing with Transformers and Hugging Face for trajectory prediction.
• ✅ Model Governance, Reproducibility, EDA, Benchmarking, Feature Engineering, and Performance Evaluation.
• ✅ Python, R, SQL, PySpark, Databricks, PostgreSQL, and MongoDB.

• Efficient Computation of the Wave Function Using Hermite Coefficient Matrix in Python — WECIQ 2024
• Smart Farming with Deep Neural Networks: DOI
• Trajectory Modeling via NLP: DOI — Teach GitHub
• Deep Learning for Trajectory Classification: DOI
• Neuroevolution for Game AI: DOI

• Data Analysis in Databricks — DataCamp

• Data Management in Databricks — DataCamp
• Databricks Concepts — DataCamp
• Introduction to Databricks — DataCamp
• Data Visualization in Databricks — DataCamp
• Credit Risk Modeling in Python — DataCamp · Aug 2025 · Credential ID: 42,185,742

• Academy Accreditation — Databricks Fundamentals
• NVIDIA Fundamentals of Deep Learning

• Certificate of Publication — Sensors
• Certificate of Publication — Future Generation Computer Systems

This experience combines academic rigor with real-world AI, delivering solutions in Machine Learning, NLP, Forecasting, Risk Modeling, Deep Learning, Data Science, and Model Governance.