Senior Data Scientist · Machine Learning Researcher · Ph.D.
Postdoctoral researcher and senior data scientist with a focus on reinforcement learning, stochastic optimization, and high-impact data products across pricing, supply chain, and financial domains.
Experience spans strategic pricing initiatives, perishable inventory research, and cross-functional leadership that bridges product, engineering, and science teams.
All the professional roles I have held, both nationally and internationally, across corporate and academic sectors.
Academic journey across applied mathematics, economics, and engineering.
Exploring reinforcement learning and dynamic programming for perishable inventory decisions.
Reinforcement learning for discrete lot-sizing and stochastic optimization problems.
Machine learning for finance with emphasis on supervised and reinforcement learning approaches.
Advanced econometrics and policy analysis supporting data-driven financial decisions.
Quantitative foundations and systems thinking applied to engineering and analytics.
Selected publications covering reinforcement learning, optimization, and forecasting.
Reinforcement learning method for stochastic environments emphasizing post-decision states.
Hybrid dynamic programming and RL approach to discrete lot-sizing on parallel machines.
Linear discrete choice models for perishable products in stochastic demand settings.
Comprehensive survey of RL strategies for algorithmic trading.
Stacked neural architectures outperforming baselines in financial time series prediction.
Reinforcement learning strategy for exercising financial options under uncertainty.
A supervised framework that surpasses reinforcement learning traders in cryptocurrency markets.
Stock-out substitution modeling for vertically differentiated perishables.
Conference contribution blending investor sentiment with reinforcement learning for adaptive trading agents.
WaveNet, RNN, and ML baselines evaluated for Bitcoin price prediction.
Study of neural networks applied to forecasting Brazilian stock returns.
Projects
Open-source initiatives translating research insights into reusable tooling.
Live activity across public repositories.
Simulation environment and heuristics for managing perishable inventory with stochastic demand.
Batch utility to rename PDF files using pattern matching and metadata extraction.
Lightweight translation interface leveraging modern NLP services for research teams.
Post-decision proximal policy optimization experiments featuring dual-critic architectures.
Contextual bandit framework combined with ResNet models for adaptive trading strategies.
Reinforcement learning agents tackling discrete lot-sizing on parallel machines.
Convolutional models to approximate optimal stopping boundaries for financial options.
Exploration of Dempster-Shafer theory for evidence aggregation in uncertain systems.
Reach out for research partnerships, data science initiatives, or speaking engagements.