Spain
As a Ph.D. and Data Scientist with experience across industry and academia in predictive modeling, statistics, and machine learning, I specialize in helping companies translate business challenges into data-driven solutions. My background includes developing novel machine learning algorithms for rare-event detection—work that earned best paper awards—as well as consulting on digital products for credit risk assessment and supporting the migration toward ML-driven decision systems in the banking sector. I have also worked in supply chain analytics and data engineering, building end-to-end workflows in Azure Databricks and AWS SageMaker, implementing forecasting reallocation logic for selling materials, and delivering business insights through AI-enabled Power BI solutions. My expertise spans supply chain, commercial strategy, operations modelling, and credit risk management. Over time, I have expanded my scope to include project management, coordinating data science teams, and deploying solutions such as AI agents for competitor analysis and digital tools for fleet management. I have also delivered data science capabilities for the first time within an organisation. I thrive in cross-functional and multidisciplinary environments, having worked in Dutch and English multinationals, a bank, consulting firm, universities, the chemistry and aviation industries. I am guided by values such as excellence, high commitment, enthusiasm, innovation, transparency, and a healthy work–life balance. I believe deeply in team spirit and collective growth, and I enjoy designing team-building activities that foster collaboration, happiness, and shared learning. As I continue to grow professionally, I am shaping a vision grounded in innovation and excellence, with a strong focus on leading data-driven initiatives. I am fully bilingual in English and Spanish (C2), have strong communication skills in French (B2), and am currently learning German (A1).
• Collaborating with insights managers to identify opportunities for process improvement and value creation through airlines data with AI methodologies for Vueling, British Airways, Iberia, Level and Aer Lingus. • Lead AI-driven initiatives, including digital products and AI agents to automate workflows, optimize airline operations, and drive business value through ML methodologies such as forecasting, propensity modeling, and process automation. • Define and implement ML architectures across the full stack—data engineering, model development, deployment, visualization (Power BI)—using AWS (SageMaker, Airflow), Snowflake, and GitHub) , JIRA/Confluence. • Develop a knowledge-sharing framework to scale data science capabilities and foster collaboration across teams.
Implement continuous improvement initiatives driven by data, including forecast reallocation logic, data quality assurance, and the identification of data anomalies to ensure effective supply chain planning across end-to-end (E2E) projects with key stakeholders, including Supply & Demand Senior Managers across EMEA and North America. Co-led the development of data science solutions for migrating supply data workflows to Microsoft Azure Databricks (Cloud), integrating Alteryx with Azure (Unity Catalog, Data/Delta Lake and Orchestration). Onboarded and trained new data scientists, focusing on enhancing their capabilities and skills for certain global data-driven projects. Explored and experiment artificial intelligence solutions to offer decision support for supply chain planners. Developed Power BI dashboards for visualization of real-time monitoring of data workflows.
Provided extensive customer support across end-to-end projects, including process observation, problem definition, solution implementation, results presentation, and verification. Led the planning, design, and technical development of the IT company's product (AIS FinRisk), coordinating its implementation across multidisciplinary teams, including data scientists, IT project managers, and clients, while utilizing agile methodologies. Developed machine learning and statistical models, focusing on traceability and interpretability, for leading Spanish banks. Solved business and financial problems using personalized machine learning and statistical algorithms with R (dplyr), SQL, Python (pyspark), Cloud, DevOps, and Adobe Analytics to reveal the customer’s digital footprint.
* Project managed and supervised university knowledge transfer projects focused on sales forecasting models (demand planning) using artificial intelligence tools in collaboration with multinational companies based in Madrid. *Awarded for teaching courses in Data Mining (data wrangling in R, predictive modelling with machine learning, clustering, text analysis, visualization, and data storytelling) and Econometrics I & II (classical modelling methods with cross-sectional and time series data).