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 Modeling
Predictive Analytics
Machine Learning
Time Series Forecasting
A/B Testing
Causal Inference
Ensemble Methods
XGBoost
LightGBM
Neural Networks
Credit Scoring
PD/LGD/EAD Modeling
Fraud Detection
Anti-Money Laundering
Behavioral Scoring
Collection Scoring
Portfolio Analytics
Stress Testing
R (Advanced)
Python (Advanced)
SQL (Advanced)
PySpark
Julia
C++
TMB
Databricks
Git
Bayesian Statistics
GLM/GAM/GLMM
Survival Analysis
Multivariate Analysis
Spatial Statistics
Bootstrap
MCMC
Maximum Likelihood
EM Algorithm
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Ph.D. in Statistics (In Progress) - PPGMNE/UFPR
- Research: Statistical Inference and Bayesian Networks
- Advisor: Wagner Hugo Bonat (PhD)
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M.Sc. in Statistics - PPGMNE/UFPR
I'm open to collaborations on: Statistical Modeling | Credit Risk | Machine Learning | R Package Development
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
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"
)