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alexis_rangelc

Alexis Rangel

@alexis_rangelc

Data Scientist and Quant Analyst

México
Espanhol, Inglês
Algumas informações são exibidas no idioma inglês.
Sobre mim
Hi, I'm Alexis — Senior Data Scientist & Quant Analyst with an MSc in Applied Statistics and a background in banking risk analytics. I build robust, production-grade data solutions: • Machine Learning & Prediction (Python, XGBoost, Scikit-learn) • Statistical Analysis & Econometrics (Forecasting, Inference) • Interactive Dashboards (Streamlit, Dash, DuckDB) • Data Engineering & ETL Automation (SQL, Pandas, PySpark) Clean code, statistical rigor, and fast turnarounds. Send me a message to get started!... Saiba mais

Habilidades

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alexis_rangelc
Alexis Rangel
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Conheça meus serviços

Consulta de ciência de dados
I will do python data analysis and machine learning models
A partir deUS$ 75 /projetoMais detalhes

Experiência profissional

BBVA_Technology

BBVA Technology

Período integral • 3 yrs 1 mo

Data Scientist Analyst

Dec 2025 - Sep 2026 • 9 mos

Focused on advanced machine learning governance, quantitative validation, and statistical performance monitoring for credit risk systems: • Designed automated monitoring pipelines to track predictive model stability, population drift (PSI/CSI), and discrimination metrics (AUC, KS, Gini). • Executed quantitative benchmarks, sensitivity analyses, and statistical backtesting protocols using Python and SQL. • Evaluated end-to-end model architectures to ensure statistical rigor, reproducibility, and alignment with governance standards. • Automated data extraction workflows and interactive executive reports to communicate technical risk metrics to key stakeholders.

Retail Risk Associate

Aug 2023 - Dec 2025 • 2 yrs 4 mos

Specialized in credit risk modeling, quantitative analytics, and predictive solutions within banking environments: • Developed and validated statistical risk frameworks and machine learning models (XGBoost, logistic regression) for retail portfolio governance. • Built high-performance data pipelines and automated analytical workflows using Python, SQL, and PySpark to process high-volume datasets. • Designed executive reporting dashboards and tracking metrics to monitor model performance, default probabilities, and data integrity. • Translated complex econometric methodologies and regulatory requirements into production-ready analytical pipelines.