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evandeilton/README.md

José Evandeilton Lopes

🎓 PhD Candidate in Statistics | 📊 Big Data Analytics | 🏦 FinTech Solutions | 💳 Risk Modeling

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👨‍🔬 Short Bio

Statistician and Data Scientist with 10+ years of experience in credit risk modeling, fraud detection, and big data analytics. PhD candidate at PPGMNE/UFPR specializing in statistical inference and machine learning applications in financial services. Expert in developing scoring models, Weight of Evidence (WoE) methodologies, and optimal binning algorithms for risk assessment. Proficient in R, Python, SQL, and PySpark with proven track record in transforming complex data into actionable business insights.

Research Interests: Bayesian Networks | Optimization | Computational Statistics | GLM/GAM | Time Series | Machine Learning | AI


Trophy

🚀 Featured Open Source Projects


gkwreg

gkwdist

OptimalBinningWoE

🎯 Core Competencies

📊 Data Science & Machine Learning

Statistical Modeling Predictive Analytics Machine Learning Time Series Forecasting A/B Testing Causal Inference Ensemble Methods XGBoost LightGBM Neural Networks

💳 Credit Risk & Fraud Analytics

Credit Scoring PD/LGD/EAD Modeling Fraud Detection Anti-Money Laundering Behavioral Scoring Collection Scoring Portfolio Analytics Stress Testing

🔧 Technical Stack

R (Advanced) Python (Advanced) SQL (Advanced) PySpark Julia C++ TMB Databricks Git

📈 Statistical Methods

Bayesian Statistics GLM/GAM/GLMM Survival Analysis Multivariate Analysis Spatial Statistics Bootstrap MCMC Maximum Likelihood EM Algorithm


🛠️ Technology Stack

Programming & Analytics

R Python SQL PySpark Julia C++

Machine Learning & AI

Scikit Learn XGBoost Tidymodels TensorFlow PyTorch

Big Data & Cloud

Databricks Azure

Databases

MySQL PostgreSQL SQL Server


📈 GitHub Analytics

Streak Stats

🎓 Academic Background


📫 Let's Connect!

I'm open to collaborations on: Statistical Modeling | Credit Risk | Machine Learning | R Package Development

Portfolio Email Google Scholar

💬 Areas of Interest for Collaboration

Research: Bayesian Networks | Causal Inference | MAchine Learning for Finance
Industry: Credit Risk Models | Fraud Detection Models | Real-time Scoring
Open Source: R Packages | Statistical Libraries | ML Frameworks


📌 Fun Facts

personal_attributes <- list(
  coffee_level = "Infinite ☕",
  coding_hours = "24/7",
  favorite_distribution = "(gkw) Generalized Kumaraswamy Distribution",
  life_motto = "In Data We Trust",
  superpower = "Finding patterns in chaos",
  weakness = "Can't resist a good dataset"
)

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