Hi, I’m Ignacio Morales.

I work with data end to end — collecting it, structuring it, and turning it into something people can actually act on. That spans building and calibrating machine learning models, pulling clean datasets out of messy real-world sources, and running experiments that separate a real effect from noise. The common thread is taking a problem from raw and ambiguous to clear and defensible.

I care about the parts that usually get skipped. Whether it’s making sure a model’s probabilities are honest and its decisions can be explained, designing a test with enough power to trust the result, or being rigorous about whether a finding is practically significant and not just statistically significant — the discipline is the same. I let the data lead instead of forcing a story onto it, and I work in Spanish and English.

What sets me apart is how I frame the problem. I treat accuracy, a clean chart, or a positive result as the starting line, not the finish — I care as much about whether the work is rigorous and defensible as whether it looks impressive. I learn fast, move easily between technical and non-technical teams, and I’m drawn to the messy problems where the right answer isn’t obvious yet.