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 churn and reactivation for the membership program of one of Brazil’s largest football clubs, 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 causal machine learning, combining applied industry problems with rigorous mathematical foundations.
Courses & Educational Projects
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Fundamentos para Inferência Causal
A Portuguese-language course for Brazilian data professionals, building Causal Inference from the ground up — from mathematics, probability, and statistical inference to regression, causal methods, and an end-to-end project taken into production. -
Mathematics & Statistics Foundations for Causal Machine Learning
A more theoretical and globally oriented project, focused on the advanced mathematical and statistical foundations behind Causal Machine Learning, connecting probability, statistical inference, econometrics, and modern causal methods.
Coming Soon
- A/B Testing & Experiment Design
- Statistical Inference for Experimentation
- Causal Inference in Observational Data
- Applied Machine Learning for Business Decisions
Selected Projects
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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.
Data & Analytics Engineering
A selection of projects from my professional experience building production data and analytics systems.