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
Bayesian A/B Test Calculator
Calculate the probability of one variant beating another using Bayesian inference with Monte Carlo simulation. Choose your model depending on your metric type.
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.
#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, 2025
#022 Causalidad en la práctica: DiD con Control Sintético (SCM)
Primera edición de septiembre. Esta vez continuamos con la serie que iniciamos en agosto sobre análisis causal. En la edición 020 introduje el concept...
Sep 7, 2025
#021 Guía: MCP de Google Analytics con Claude Desktop
La actualidad manda. Sé que dije que no publicaría ninguna nueva edición hasta septiembre, pero este anuncio (¡que me pilló en plenas vacaciones! ¡vay...
Aug 3, 2025Let's talk
Thinking about experimentation, causal inference, or AI-driven decision systems? Drop me a line or find me on LinkedIn.