Growth, Product & Customer Analytics
Using acquisition, activation, engagement, and lifetime-value analysis to guide customer, product, and commercial decisions.
Hi, I'm Gabriel Reynoso
Data Scientist | Customer Experimentation | Data & Analytics Engineering
I work with data to understand customers, test ideas, and help teams make informed decisions.
About Me
My background brings together business analytics, experimentation, and data engineering. I enjoy understanding a problem and building what’s needed to answer it.
Across fintech, retail, and automotive, I have built analytical foundations from the ground up: data warehouses and models, automated pipelines, customer funnels, LTV frameworks, attribution systems, and decision tools for Growth, Product, Finance, Operations, and leadership teams.
In fintech, I led analytics teams across Customer Engagement, Referral, lifetime value, and Savings. My work combined experimentation, causal inference, predictive modeling, and analytics engineering to improve acquisition efficiency, customer value, and product decisions.
I hold degrees in Mechatronics Engineering and Industrial Engineering from ITAM in Mexico and am currently completing an MS in Data Science at Stevens Institute of Technology in the United States. Outside work, I am an avid reader and sports enthusiast who enjoys exploring new ideas, discussing them thoughtfully, and continuing to learn.
Technical Toolkit
Experimentation, Causal Inference & ML
Designing experiments, evaluating causal effects, and building predictive models that support targeted and measurable decisions.
Data & Analytics Engineering
Building reliable data models, pipelines, warehouse workflows, and measurement systems that make analytics useful in everyday operations.
Technical Skills
The tools and methods I use to explore data, build solutions, and support business decisions.
Programming, Analysis & Visualization
Experimentation, Causal Inference & ML
Data & Analytics Engineering
LLM Applications & AI-Assisted Development
Featured Projects
A selection of projects in data science, optimization, and practical AI tools.
FIFA World Cup Forecasting
A two-stage probabilistic forecasting system for the 2026 World Cup, combining a Dixon-Coles match model with a LightGBM calibrator and a Monte Carlo tournament simulation.
- 0.8711 test log-loss, +20.7% vs. uniform baseline
- Evaluated on 4,552 held-out matches (2022+)
- 100,000-run Monte Carlo tournament simulation
Traffic Optimization via Collective Intelligence
Comparing greedy routing, marginal-cost assignment, and a Q-learning agent trained by expert imitation on a real Manhattan street network.
CodeKindle Highlights to Obsidian
Turns Kindle highlights into organized Obsidian notes, using AI to group ideas by theme and semantic matching to reduce duplicate content.
- Book overview and linked thematic notes
- Semantic duplicate detection scoped to each book
- Structured AI outputs with rate limiting and retries