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

Statistical ModelingPredictive AnalyticsMachine LearningTime Series ForecastingA/B TestingCausal InferenceEnsemble MethodsXGBoostLightGBMNeural Networks

Credit ScoringPD/LGD/EAD ModelingFraud DetectionAnti-Money LaunderingBehavioral ScoringCollection ScoringPortfolio AnalyticsStress Testing

R (Advanced)Python (Advanced)SQL (Advanced)PySparkJuliaC++TMBDatabricksGit

Bayesian StatisticsGLM/GAM/GLMMSurvival AnalysisMultivariate AnalysisSpatial StatisticsBootstrapMCMCMaximum LikelihoodEM Algorithm

• Ph.D. in Statistics (In Progress) - PPGMNE/UFPR

  • Research: Statistical Inference and Bayesian Networks
  • Advisor: Wagner Hugo Bonat (PhD)

• M.Sc. in Statistics - PPGMNE/UFPR

  • Thesis: Beta regression models for scaled data (Modelos de regressão beta para dados de escala)

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