Data Scientist with over 4 years of experience applying machine learning techniques and data analysis to solve real-world business problems with measurable impact.

Throughout my career, I worked on projects involving the extraction and processing of structured and unstructured data, predictive modeling for credit scoring and sales forecasting, anomaly detection, and customer behavior analysis — always with a focus on turning data into actionable insights.

I have hands-on experience with libraries such as scikit-learn, spaCy, pdfplumber, pytesseract, and pandas, as well as techniques like NLP, clustering, and supervised learning.

• 🎓 Master's degree in Natural Sciences (UENF) and Bachelor's degree in Physics (IFF)
• 📊 Experienced in predictive modeling, clustering, and NLP
• 🛠️ Skilled with Python, scikit-learn, spaCy, pandas, pdfplumber, pytesseract
• 🖥️ Hands-on experience with unstructured data extraction and interactive dashboards in SAS
• ✍️ I share technical content on Medium

• Developed solutions for unstructured data extraction (PDFs and images) using pdfplumber, Tesseract OCR, regex.
• Built interactive SAS dashboards for time series analysis and anomaly detection.
• Applied NLP (spaCy) to analyze social media comments, identifying customer concerns and dissatisfaction patterns.

• Built credit scoring models, sales forecasting, and customer segmentation using machine learning and clustering.
• Developed predictive models for customer behavior (default risk, plan upgrade likelihood, job instability).
• Delivered insights supporting strategic decision-making and risk reduction.

• Processed structured and unstructured data using regex and data cleaning techniques.
• Implemented outlier detection algorithms based on business rules for risk mitigation.
• Created ML models for delivery delay prediction, improving logistics operations.