- MAPFRE (Desarrollo de negocio Digital)Data Eng (Business, Data + AIBANCA & SEGUROSseptiembre de 2024 - Hoy (3 meses)Madrid, España- AWS - GCP data migration- Adobe CDP- GCP BigQuery-Data migration: I planned and executed the migration of large volumes of data, ensuring the integrity and quality of information throughout the process.-Database construction: I designed and developed highly scalable data tables and cubes in Google Cloud, optimising the structure for advanced analytics and agile queries.-Interactive dashboard creation: I migrated and enhanced existing Power BI dashboards to Google Cloud, incorporating new visualisations and functionality to facilitate data exploration and actionable insights generation.-Data exploration and machine learning model development: I performed a deep exploratory analysis of the data, identifying relevant patterns and trends. From these insights, I developed machine learning models to predict [mention specific use cases, e.g. customer churn, fraud detection, etc.].-Implementation of data products: I collaborated closely with business teams to transform the insights obtained into tangible data products, which enabled process optimisation and improved decision making.
- Google CampusAI Researcherabril de 2024 - julio de 2024 (4 meses)AI Researcher - Google CampusDeveloping an AI-Driven Antibiotic Sensitivity Prediction Model to determine bacterial susceptibility toantibiotics based on various data sources including metabolomic profiles (potentially derived frommass spectrometry).Employed machine learning techniques like supervised learning (logistic regression, decision trees)and deep learning (CNNs, RNNs) to analyze biological data and predict antibiotic resistance.Utilized MLOps principles to streamline the model development process, ensuring efficientdeployment into production environments.Key data:• Genome• Proteome• Phenotype• Susceptibility tests• Clinical informationAI Techniques• Machine Learning:o Supervised learning: Algorithms such as logistic regression, decision trees, orartificial neural networks are used to predict the sensitivity or resistance of abacterium to an antibiotic based on its characteristics and the properties of theantibiotic.o Unsupervised learning: Techniques such as clustering are used to identifygroups of bacteria with similar resistance patterns.• Deep Neural Networks:o Convolutional neural networks (CNN): Used to analyze electron microscopyimages and detect morphological features associated with resistance.o Recurrent neural networks (RNN): Applied to analyze genomic sequencesand predict the presence of resistance genes.• Reinforcement Learning:o Models can be developed that learn to select the best antibiotic treatmentbased on the patient's characteristics, the bacterium, and the results obtained.
- Abbott Laboratories (UK)Business Intelligence @ Cardiac Rhythm ManagementINDUSTRIA FARMACÉUTICAseptiembre de 2023 - marzo de 2024 (7 meses)•Define the Sales Strategy and KPIs from a deep data analysis (Sales Revenue, Mkt Share, Service& Support)• Design and day to day of different BI reports: PowerBI & Looker, SAP BO• Business unit development & sales management, marketing and education activities supporting theregional distributor.
- Master of BusinessESCP EUROPE2015MBA, Master of Business Administration
- Master of Data ScienceRey Juan Carlos University2014Master in Data Analysis
- Bachelor of Business AdministrationUniversidad Complutense2012Bachelor in Journalism & Business Administration
- SalesForceSalesforce2022