Data Scientist · AI & Computer Vision · Data Engineering · BI & Analytics
My profile, portfolio, and blog—in one place.
Economist by training, data scientist in practice—7+ years building pipelines, models, and BI in banking, SaaS, and AI programs. This site is my profile, a portfolio of real work, and a blog where I think out loud about data, ML, and economics.
Structure messy data into stable signals—measured, documented, ready for the next step.
Estimate scenarios with models you can inspect; probability as a tool for judgment.
Turn analytical output into views teams actually use—fewer KPIs, clearer ownership.
Name tradeoffs and constraints before choosing—technical work connected to purpose.
Production CV data pipelines—extraction, cleaning, annotation, and evaluation for image and video models.
Start here
Three ways in: who I am (profile), what I've built (portfolio), and how I think (blog).
Most projects start by stabilizing messy inputs, then estimating with models you can inspect, then shipping interfaces people run week to week. When incentives or constraints shape the answer, I make that explicit before anyone treats a chart as policy.
Signal, model, decision
Hand-picked case studies — Problem · Process · Impact
Educational open data on datos.gov.co was hard to extract and reuse at scale for ICFES analysis.
Connected the SODA API with Python (sodapy), loaded results into BigQuery, and built Data Studio views.
Reproducible ICFES 2019-2 pipeline—less manual extraction, faster exploration for decision support.
2016 plebiscite results were scattered across sources—hard to compare municipalities and departments consistently.
Python scraping workflow to aggregate territorial results and publish them in Google Data Studio.
Reproducible municipality-level vote intelligence for political and policy analysis.
Automation risk in Colombia needed local evidence beyond US-centric occupation probability studies.
Adapted Frey & Osborne-style modeling with World Bank STEP survey data and econometric adjustment in Python.
Policy-relevant read on skills, automation exposure, and sector-level labor market structure.
Everything I publish, newest first.
A quoted loan rate describes nominal cash flows. It does not show how the payment burden changes after inflation. This note connects three views of the same ...
A calculadora de inflación Colombia is useful only when its source, reference months, and formula are visible. This implementation turns the official monthly...
Cloud chatbots are easy to demo and hard to trust with internal notes. RAG means Retrieval Augmented Generation: before the model answers, you fetch a few re...