Data Analyst · SQL · Python · Power BI · Business & Operations Analytics

Background in business operations, now focused on data. I combine domain knowledge in sales, costs, and margins with analytical rigor in Python, SQL, and BI. I don't just extract value from data — I understand what value looks like because I've operated where it's created.

Every project here produces measurable impact: predictive models that businesses trust, dashboards that inform critical decisions, data pipelines that transform raw inputs into actionable intelligence.

• Operational Intelligence — Translating business metrics into data strategies that drive real change
• End-to-End Solutions — From data extraction to delivery: APIs, dashboards, ML models, automated pipelines
• Domain Expertise — Understanding sales, costs, margins, forecasting because I built the systems that manage them
• Statistical Rigor — Hypothesis testing, validated models, clean pipelines, transparent methodologies

Enterprise Climate Intelligence Platform

• AEMET OpenData integration, crowdsourcing, smart alerts
• 158 automated tests with CI/CD pipeline
• Real-time monitoring, predictive analytics
• Stack: FastAPI · PostgreSQL · Streamlit · Docker

AI-Powered Life Cycle Assessment

• ISO 14040/14067 compliant LCA automation
• LangChain + Gemini for intelligent document processing
• Generates sustainability seals and QR codes
• Stack: FastAPI · LangChain · Gemini · FAISS · PostgreSQL

EU Telecom Network Stress Simulation

• 41,937 rows CNMC regulatory data + 1.8M+ Eurostat macrodata
• 5/6 hypotheses confirmed on market dynamics
• Traffic CAGR: +127% vs Revenue CAGR: -0.4% (Scissors Effect)
• Stack: Python · Pandas · Power BI · DuckDB · Docker

Multi-Market Logistics Intelligence

• 15+ KPIs, 5+ data sources, multi-regional adaptability
• ML cost prediction model (R² = 0.987)
• End-to-end ETL pipeline from acquisition to visualization
• Stack: Python · Pandas · Streamlit · Plotly · PostgreSQL

Collaborative Rural Logistics Platform

• 335 matches between producers and transporters
• 3.5T CO₂ emissions reduction
• Coverage of 360 municipalities in Spain
• OSRM + SQLite integration
• Stack: Python · Dash · OSRM · SQLite

Rural Commerce Network for Travelers

• PWA mobile app for vanlifers and caravanists
• Map-based discovery (Leaflet + OpenStreetMap)
• QR check-in, magic link authentication
• Stripe integration for premium features
• Stack: Next.js · TypeScript · Prisma · Supabase · Stripe

Hotel Revenue Management

• Random Forest + Optuna hyperparameter tuning
• +30% net revenue improvement vs OTA at 30% commission
• AI-driven dynamic pricing reducing commission dependency
• Stack: Python · scikit-learn · Optuna · Streamlit

Digital Attention Economics

• 16,402 posts from 7 platforms, 4,779 users analyzed
• NLP sentiment prediction and engagement modeling
• Global Gini coefficient: 0.974 (extreme concentration)
• Power law analysis (Pareto principle across digital platforms)
• Stack: Python · Polars · Transformers · Streamlit · scikit-learn

Intelligent Web Scraper API

• Selenium + Gemini for dynamic website scraping
• Structured data extraction (name, price, images, URLs)
• Handles JavaScript, cookies, anti-bot measures
• Docker containerization for deployment
• Stack: FastAPI · Selenium · Gemini · Docker

90s Nostalgia AI Generator

• Google Gemini LLM for nostalgic storytelling
• REST API with Swagger documentation
• SQLite persistence, Docker deployment
• Stack: FastAPI · Gemini · SQLite · Docker

10,000+ Spanish Wines Analysis

• 60+ regions, 10,000+ wines comprehensive EDA
• Price-rating correlations and regional insights
• Statistical models identifying quality-value relationships
• Stack: Python · Pandas · Matplotlib · Seaborn · Jupyter

• Predictive Modeling: R² > 0.98 on cost forecasting (DashLogistics)
• Scale: 16K+ data points analyzed across 7 digital platforms
• Efficiency: Automation of manual reporting processes reducing time-to-insight by 80%
• Impact: 3.5T CO₂ savings through optimized logistics matching
• Validation: 5/6 scientific hypotheses confirmed in telecom analysis
• Deployment: Production-ready APIs and dashboards serving business needs

• Domain-driven — I come from operations. I know what margins, costs, and forecasts mean because I've worked with them directly.
• Technical — Python, SQL, Power BI, ML. I build pipelines, models, and dashboards end-to-end.
• Outcome-focused — Every project here has a measurable result: R² scores, CO₂ saved, revenue impact, hypotheses tested.

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