Wilson

Experiencia demostrada en Python.

Experiencia demostrada en SQL.

Experiencia demostrada en Bash/Terminal.

Experiencia demostrada en Git.

Experiencia demostrada en GitHub.

Designed and validated quantitative performance metrics under uncertainty, using simulation, statistical testing and reproducible Python workflows to support decision-making.. Built large-scale Monte Carlo tools to estimate distributions, variance, tail risk and performance stability across thousands of simulated scenarios.. Developed regime-switching volatility models with Hidden Markov Models and variance-decomposition frameworks to explain performance beyond static IID assumptions.. Implemented validation batteries and visual diagnostics to audit critical variables, detect inconsistencies and communicate statistical risk.

Developed predictive pipelines and scenario-analysis tools in Python for complex spatiotemporal systems using real-world Argentine Ministry of Health data.. Applied Bayesian estimation and rolling-window optimization to calibrate models under uncertainty and improve fit versus static baselines.. Reduced simulation costs by 25% through optimized numerical algorithms and more efficient parameterization of repeated scenarios.. Communicated model assumptions, uncertainty, validation results and limitations through research papers, presentations and interdisciplinary work.

Designed and delivered university-level lectures and practical workshops on probability, statistics and their application to artificial intelligence.. Prepared educational material, problem sets and assessments focused on inference, uncertainty and quantitative modeling.

Built a Random Forest demand-forecasting model for passenger ticket sales, increasing forecast accuracy by 30% through improved planning and allocation.. Translated operational data into forecasts for capacity allocation and commercial decision-making.