Data Scientist focused on Experimentation, Causal Inference, and Machine Learning, with 6+ years of experience across applied data science, data engineering, and quantitative research.
My work combines statistical reasoning, predictive modeling, causal inference, and production data systems. I have built machine-learning models for customer reactivation, conducted applied econometric research at UCLA and Royal Holloway University of London, and contributed to a World Bank research project on climate change and agricultural productivity.
I also bring a strong engineering background, with hands-on experience building production analytics systems using Python, SQL, dbt, Snowflake, Databricks, Redshift, and AWS, allowing me to work from reliable data foundations through modeling, measurement, and decision-making.
- Academic Training:
- Mechanical Engineer from UFRJ, where I graduated cum laude and was awarded the General Electric Scholarship
- Master in Economics & Public Policy from UCLA, awarded the Lemann Foundation Scholarship and the Luskin Scholarship
- (unfinished) doctoral training in Economics at Tilburg University in the Netherlands, awarded the Koopman’s Scholarship
- Current Focus:
- deepening my work in Experimentation, Causal Inference, and Causal Machine Learning
- Beyond Work:
- built a community of 6,000+ followers on LinkedIn
- lived and worked across the USA, Europe, and Brazil
- continue to play competitive amateur football wherever I go
Experimentation & Causal Inference
My current focus is on experimentation, causal inference, statistical reasoning, and applied machine learning.
Coming soon:
- A/B Testing & Experiment Design
- Statistical Inference for Experimentation
- Causal Inference in Observational Data
- Applied Machine Learning for Business Decisions
Selected Projects:
-
Causal Inference: Customer-Satisfaction Program
Estimated the causal impact of a customer-satisfaction initiative using Difference-in-Differences to separate program effects from underlying time trends. -
Causal Inference: Recommendation System Impact
Estimated the impact of a recommendation system using Propensity Score Matching + Difference-in-Differences, supporting product evaluation with observational data.
Mathematics, Statistics & Causal Machine Learning
- Foundations Mathematics and Statistics for Data Science & Causal Machine Learning
I am developing an open educational project on the mathematical and statistical foundations of Causal Machine Learning.
Data & Analytics Engineering
A selection of projects from my professional experience building production data and analytics systems.