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I am a data scientist with a solid statistical foundation and hands-on experience in predictive modeling (machine learning, deep learning) and mathematical optimization. I specialize in building AI architectures, with a recent focus on LLMs and retrieval-augmented generation for conversational solutions. Currently an AI & Advanced Analytics Intern at Grupo Energisa, I improved credit risk scoring with XGBoost and logistic regression (+10% accuracy), engineered large-scale data pipelines with SQL/Spark, and developed MILP optimization for fleet routing (+20% logistic efficiency). I also automated data handling with Python-based RPA and supported teaching in statistics and probability at UFPB.…I am a data scientist with a solid statistical foundation and hands-on experience in predictive modeling (machine learning, deep learning) and mathematical optimization. I specialize in building AI architectures, with a recent focus on LLMs and retrieval-augmented generation for conversational solutions. Currently an AI & Advanced Analytics Intern at Grupo Energisa, I improved credit risk scoring with XGBoost and logistic regression (+10% accuracy), engineered large-scale data pipelines with SQL/Spark, and developed MILP optimization for fleet routing (+20% logistic efficiency). I also automated data handling with Python-based RPA and supported teaching in statistics and probability at UFPB.

Gabriel Estrela Lopes

Data Scientist, AI Engineer, Full Stack Developer, +2

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I am a data scientist with a solid statistical foundation and hands-on experience in predictive modeling (machine learning, deep learning) and mathematical optimization. I specialize in building AI architectures, with a recent focus on LLMs and retrieval-augmented generation for conversational solutions. Currently an AI & Advanced Analytics Intern at Grupo Energisa, I improved credit risk scoring with XGBoost and logistic regression (+10% accuracy), engineered large-scale data pipelines with SQL/Spark, and developed MILP optimization for fleet routing (+20% logistic efficiency). I also automated data handling with Python-based RPA and supported teaching in statistics and probability at UFPB.…I am a data scientist with a solid statistical foundation and hands-on experience in predictive modeling (machine learning, deep learning) and mathematical optimization. I specialize in building AI architectures, with a recent focus on LLMs and retrieval-augmented generation for conversational solutions. Currently an AI & Advanced Analytics Intern at Grupo Energisa, I improved credit risk scoring with XGBoost and logistic regression (+10% accuracy), engineered large-scale data pipelines with SQL/Spark, and developed MILP optimization for fleet routing (+20% logistic efficiency). I also automated data handling with Python-based RPA and supported teaching in statistics and probability at UFPB.

Available to hire

I am a data scientist with a solid statistical foundation and hands-on experience in predictive modeling (machine learning, deep learning) and mathematical optimization. I specialize in building AI architectures, with a recent focus on LLMs and retrieval-augmented generation for conversational solutions.

Currently an AI & Advanced Analytics Intern at Grupo Energisa, I improved credit risk scoring with XGBoost and logistic regression (+10% accuracy), engineered large-scale data pipelines with SQL/Spark, and developed MILP optimization for fleet routing (+20% logistic efficiency). I also automated data handling with Python-based RPA and supported teaching in statistics and probability at UFPB.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Advanced

Work Experience

Data Scientist (AI & Advanced Analytics Intern) at Grupo Energisa

August 1, 2024 - August 1, 2025

Predictive & Credit Risk Modeling: Engineered supervised ML classification models (XGBoost, Logistic Regression) for financial default scoring. Applied SMOTE and hyperparameter tuning, resulting in a 10% accuracy increase in detecting high-risk profiles within the credit market. Data Engineering & SQL: Architected massive data pipelines using SQL, Apache Spark, and PySpark to process both structured and unstructured datasets. Applied clustering to compress strategic database volumes by 90%, optimizing model ingestion processes. Operations Research: Developed MILP algorithms in Python for optimized fleet routing, boosting logistic efficiency by 20%. Automation: Built Python-based RPA bots, saving 2 man-hours daily and eliminating manual errors in data handling.

Teaching Assistant (Statistics and Probability) at Federal University of Paraíba (UFPB)

February 1, 2024 - February 1, 2025

Delivered practical sessions in Descriptive Statistics, Inference, and Hypothesis Testing (A/B testing), raising pass rates by 50%.