I am Diego Aguiar,
Sr. Data Analyst | Economics MSc with Technical & Business Insight.

Hi! I'm Diego, an economist and senior data analyst based in Montevideo, Uruguay. I hold two master's degrees in Economics from U.S. universities and have over five years of experience using data to solve business problems, especially in fraud prevention and customer behavior. I currently work at Mercado Libre, where I build analytics tools to reduce fraud and optimize business performance. I'm passionate about applying data science to real-world challenges and currently expanding my skills through a postgraduate specialization in Data Science and Artificial Intelligence (UTEC + MIT). I'm fluent in Python, SQL, and R, and I enjoy combining technical work with business insight. In the past, I’ve also taught university-level courses including Principles of Macroeconomics, Intermediate Macroeconomics, Calculus, Statisics and Applied Data Science.

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Projects

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House Price Prediction in Uruguay

Machine learning project focused on predicting house prices in Uruguay using real estate listings data from Mercado Libre and El Gallito (2023–2025). The analysis includes data cleaning, outlier treatment, feature engineering, and model comparison (Linear Regression vs CatBoost), with hyperparameter tuning using Optuna. Best performance is achieved for Montevideo, with a MAPE of approximately 25%.

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Montevideo Moves (Spanish)

In this project, we analyze traffic incidents in Montevideo using real-world data from the Uruguayan government. The goal is to identify critical patterns (age, time, location, vehicle type) and propose public policy based on data. Includes Python code, visualizations, a full presentation, and a final report for the UTEC+MIT specialization.

Student Performance

In this project, I use Python to analyze student performance using a dataset from Kaggle. I explore which factors best explain GPA through exploratory data analysis and econometric modeling. Finally, I build predictive models using linear regression and decision trees, and compare their performance.

Covid 2021

Built an interactive Tableau dashboard to visualize key pandemic indicators such as confirmed cases, mortality, and regional trends. Data was cleaned and queried using SQL, with a focus on real-time updates and storytelling for exploratory public health insights.

Customer Exploration for Marketing Campaign

Conducted an exploratory analysis in R to understand customer segmentation and response behavior in a direct marketing campaign.

US Presidential Elections

Developed interactive maps and visualizations in R to explore geographic and demographic patterns from the US elections. Focused on how states changed their preferences over time.

Reproduction Rights

Used Google Trends API data and R to analyze search interest in reproductive health topics. Connected spikes in searches to policy announcements and court rulings, highlighting the relationship between legal change and public behavior.

Regression Discontinuity

Replicated Carpenter & Dobkin (2009) using R to demonstrate a regression discontinuity design applied to Medicare eligibility. Focused on visual and statistical identification strategies for estimating causal effects using a sharp cutoff around age 65.

Difference in Differences (DiD)

Reproduced the famous Card & Krueger (1994) paper using R to evaluate minimum wage effects through a difference-in-differences design. Compared employment changes in fast food restaurants between New Jersey and Pennsylvania, showcasing DiD intuition and assumptions.

Get In Touch

Feel free to contact me.