About
Six years across fintech, a Latin American unicorn, and a global consumer brand. Always on the data and ML side. I care about fast feedback loops, measurable outcomes, and code that people actually use in production.
Kueski Biggest BNPL Fintech in Mexico
I owned the full lifecycle of Kueski's production fraud prevention ML systems, covering feature engineering, model training, deployment, and ongoing monitoring. I re-engineered the definition of fraud itself, cutting false positives by 65% and reducing monthly fraud-related losses by 70%. I also introduced graph analysis to surface hidden connections between fraudulent users, adding a structural intelligence layer that static models alone couldn't capture.
Heineken Global CPG · B2B E-commerce
Heineken had just launched a B2B e-commerce platform and needed someone to turn data into decisions. I designed and ran an experiment on a subscription business model that produced a 16% revenue increase. I also built an NLP pipeline to classify and route customer reviews, compressing what used to be a two-week manual process into under two minutes. I also led educational sessions across non-technical stakeholders to build data literacy at scale.
Rappi Unicorn · On-demand Super App
At Rappi I focused on the marketing side of growth — building the attribution model that gave the team clear visibility into channel ROI and guiding spend allocation across media. I ran a series of A/B tests evaluating media vendors, creatives, and placements, using causal impact analysis to give the marketing team a rigorous way to assess performance beyond last-click assumptions.
Insaite AI Startup · Mexico
At Insaite I sat at the intersection of data science and business development. I worked directly with potential clients — understanding their data landscape, identifying untapped opportunities, and designing concrete proposals to solve them. My role was to translate complex ML capabilities into a clear business case: scoping the solution, building the work plan and Gantt, and presenting to stakeholders and C-level clients. It taught me that the hardest part of data science isn't the modeling. It's the framing.
Axity Digital Transformation Consultancy
My first professional role in data. I worked on exploratory data analysis and feature engineering for client projects, building the foundational instincts for understanding messy real-world datasets. This is where I learned to ask the right questions of data before touching a model, a habit that has shaped how I approach every problem since.
Master of Data Science
University of British Columbia · Vancouver, BC · 2025–2026
Data Science & Machine Learning Certification
Massachusetts Institute of Technology · 2022
Bachelor of Actuarial Sciences
Universidad Nacional Autónoma de México · Mexico City · 2020
Open to interesting problems. Find me on LinkedIn or read my work on Medium.