Profile
- I specialize in forecasting and statistical modeling, with a particular focus on time series.
- My background in economics and data science, together with experience working with alternative, unstructured, and high-dimensional data, broadens the range of quantitative problems I can tackle and the perspectives I bring to them.
- I combine this expertise with data engineering and software development practices to build reliable analytical pipelines and decision-support solutions.
- Author of R for Economic Research.
Areas of Expertise
Modeling: Forecasting · Econometrics · Machine Learning · State-Space Models · Bayesian Inference
Data: Time series · Alternative, Unstructured, and High-Frequency Data
Delivery: APIs · Data Apps · Automated Reports
Workflows: Data Pipelines · LLM Integration
Tech stack: R · Python · Shiny · Quarto · DuckDB · PostgreSQL · dbt · Git · Docker · Shell · CLI Tools
Professional Experience
- Led the Data Science and Modeling team within Macroeconomic Research, providing technical direction and overseeing the development of quantitative models and analytical products.
- Developed and evaluated forecasting and nowcasting models for inflation and economic activity using alternative, high-frequency, and unstructured data.
- Designed and implemented models to estimate latent economic variables, including potential output and the NAIRU, at scale across dozens of countries.
- Built end-to-end analytical workflows to operationalize statistical and machine learning models, integrating LLMs and automated data pipelines to deliver scalable analytical outputs.
- Developed forecasting models for revenues of publicly listed companies, combining economic and company-level data to support investment research.
- Built machine learning models using credit-market data to predict corporate bond prices.
- Designed and implemented forecasting models for COVID-19 cases and vaccination coverage, developing modeling approaches for rapidly evolving processes with limited historical data.
- Developed economic activity and inflation indicators using alternative and unstructured data, expanding the information available for macroeconomic analysis.
- Conducted applied economic research and monitored regional economic activity using official and administrative data.
- Developed statistical indicators and analytical tools to measure economic conditions and support public-policy discussions.
- Produced reports and presented quantitative findings to technical and nontechnical audiences.
Education
M.S. in Economics, Universidade Federal Fluminense · 2017
B.S. in Economics, Universidade Federal Fluminense · 2013
Publication
Author of R for Economic Research, a practical guide to data workflows, statistical modeling, time series, and forecasting for applied research. The book has received more than 55,000 views from readers in over 50 countries.
Selected Presentations
Nowcasting When Predictors Keep Changing: Leveraging Cross-Sectional Information in High-Dimensional, High-Turnover Data — International Symposium on Forecasting · Montreal · 2026
Nowcasting Inflation in Brazil Using Web Data — International Symposium on Forecasting · Charlottesville · 2023
Real-Time Forecasting of COVID-19 Cases Across US Regions — International Symposium on Forecasting · Remote · 2020