Experimentation.
Causal Inference.
Business Impact.
I lead teams that design experiments, measure what matters, and build evidence-based systems that drive product growth.
Head of CRO & Experimentation
LIN3S Consultora Digital
I design experimentation programs and measurement systems for banking, insurance, energy, food & beverage, fashion and luxury, and sports & entertainment clients. My work sits at the intersection of statistics, product thinking, and team leadership: helping organizations build the right experiments, measure what matters, and turn results into business decisions. Today I lead a 20+ person cross-functional team across data, product, CRO, UX, and web/app development.
I believe experimentation and data is a means, not an end. It's a tool that belongs to the entire organization, not just the data team.
Focus Areas
Industries
Location
Barcelona, Spain (Remote)
Current Focus
Right now I'm focused on what comes after traditional A/B testing: frequentist/Bayesian experimentation, causal inference, and AI agents that automate decisions at scale.
The full pipeline: from business to data and business again.
Open-source tools
Production-ready calculators and analysis notebooks for experimentation and causal inference.
Application Tools
False Positive Risk Calculator for A/B Tests
p=0.05 does not mean 5% chance of a false positive. Know the actual probability that your significant result is a fluke, given your prior, power, and observed p-value.
Code & Notebooks
PyDay-BCN-2025
7Causal Inference: a brief introduction. Covers treatments, counterfactuals, DAGs, confounding, and real cases with DiD and Synthetic Control Methods.
Bayesian-AB-Test-Calculator
4Simple Bayesian A/B Test Calculator - compute posterior distributions and probability of being best for your experiments.
MDE-calculator-AB-test
1Minimum Detectable Effect calculator for A/B testing projects to calculate your sample size properly.
Books
Two published books with Anaya about Online Controlled Experiments and Applied Data Science with Python.
Experimentación Online
A comprehensive guide to online experimentation covering the technical, statistical, and organizational foundations needed to run experiments at scale. Introduces the APPA framework (Analysis, Plan, Practice & Action) for coordinating experiments in digital environments: from hypothesis design to measuring causal impact.
Analítica de datos con Python para Marketing Digital
A hands-on guide for digital marketers who want to unlock the power of Python for data analytics. Covers the full stack: from Pandas and NumPy for data wrangling, to scikit-learn and statsmodels for machine learning, to SQL, BigQuery, Power BI and data visualization for building business KPIs.
Recent talks
Conference sessions on experimentation, CRO, and data-driven decision making.
Latest writing
On experimentation methodology, causal inference, and building decision systems.
Towards Data ScienceEnglishHow I Built a Multi-Agent System for Interrupted Time Series Analysis (ITSA)
Measuring an intervention nobody randomized: the statistics behind ITSA and the two-agent architecture that runs it — deterministic maths, one schema-validated contract, an LLM kept to reasoning.
Sep 17, 2026
Cómo construir un sistema multi-agente para Interrupted Time Series Analysis (ITSA)
Hacía tiempo que no escribía por aquí. De hecho, Leanalytics ha pasado a llamarse www.experimentaciononline.com y ocupa ese nombre como dominio dentro...
Sep 24, 2026
#023 Branding loves data (con Juanma Barea)
¿Pero qué hace Ubaldo hablando de Branding en Leanalytics? Soy Juanma Barea, responsable de Marcas B2B y Corporativas y creador la Newsletter Brandket...
Oct 19, 2025Let's talk
Thinking about experimentation, causal inference, or AI-driven decision systems? Drop me a line or find me on LinkedIn.