I'm a technical lead and data scientist based in Madrid, passionate about artificial intelligence, advanced analytics, and generative models. I lead technical strategy for AI projects, coordinate teams, and deliver production-ready solutions that drive business value. With experience in both research and industry, I specialize in time series forecasting, LLMs, agents, and data platform architectures.
Lead the technical strategy for AI and advanced analytics projects, defining pipelines, agent workflows, and data platform architectures. Coordinate teams of 2-4 people, promoting best practices in ML, analytics, and generative models. Oversee and implement strategic forecasting and agents projects, deployed in production to optimize operational planning and automate KPI extraction. Direct collaboration with clients in services, energy, and retail, ensuring technical solutions deliver business value. Improved internal processes by integrating LLMs and agents for reporting and data analysis, increasing efficiency and consistency of results.
Improved demand forecasting accuracy and reliability across multiple industries, driving better inventory planning and resource allocation. Streamlined data integration and reporting processes by automating ETL workflows, reducing manual effort and delivery time. Delivered AI-driven insights and intelligent agents that enhanced decision-making for key clients. Partnered with cross-functional teams to translate business objectives into scalable analytics solutions adopted in production.
Analysis of actigraphy time-series data in patients with brain injuries using statistical methods and ML for clinical outcome prediction. Development of preprocessing pipelines and feature extraction from wearable devices, improving data quality and consistency. Collaboration with researchers and medical staff to interpret results and contribute to study conclusions.
Relevant courses: Statistics, Machine learning, Artificial intelligence in business, Data architecture, Files and databases, Genetic algorithms.
Erasmus program: Exchange semester at Warsaw University of Technology.
Winner of the Madrid Engineering Week Datathon: Developed a data-driven solution to identify and optimize operational inefficiencies.
Data analysis to uncover key insights and optimization opportunities. This project secured my team the first place at the Madrid Engineering Week Datathon.
Developed a machine learning model with NLP and text mining to predict salaries based on job titles and descriptions.
Developed a deep learn ing model to forecast employee departures, by analyzing workforce data to uncover patterns leading to departures.
Designed and implemented a MongoDB-based NoSQL database for artist, concert, and recording data, then optimized the system with a cluster design and developed data pipelines for migration from legacy databases.
Designed and implemented an SQL database for a hospital management system and conducted performance analyses across various use cases to identify and propose optimizations.
Leveraged advanced machine learning techniques to optimize solar panel efficiency by analyzing climatic data and developing predictive weather models.
Designed and implemented an expert system to recommend vehicles for specific target markets, creating adaptive models to analyze customer preferences and vehicle features.
Conducted a detailed data analysis and visualization project to identify factors influencing high blood pressure.
Analyzed and visualized the range of computer science courses available on a selected website, using data analytics to assess course diversity, focus areas, and accessibility.