UNED

MSc, Data Science and Data Engineering

Isabel I University

Master's, Business Administration (Business Analytics & Big Data)

Rey Juan Carlos University

BSc, Biomedical Engineering

Data Scientist - Data Science and Innovation Department, IQVIA

• Asymmetric Matching: Engineered a semantic matcher from scratch, reducing computational complexity from hours to minutes while significantly boosting predictive performance by orders of magnitude with respect to the previously standard solution; now deployed globally.
• Time-Series Automation: Designed a model factory from scratch to automate pharmaceutical market analysis of product demand and pricing using Optuna for auto-optimization of naive and linear-econometric models, including static and dynamic covariates generation, streamlining delivery timelines.
• Technical Leadership: Established department-wide engineering best practices, including rigorous documentation, unit testing, and pipeline automation to enable project scaling and ensure better maintainability in the future.
• Interdisciplinary Impact: Partnered with teams to deliver high-impact tools for Doctor Targeting and hierarchical Bayesian modeling/Bayesian Structured Time Series modeling for demand product datasets in the context of scarce data.

Data Scientist & AI Researcher - Delivery and Research Department, Sherpa.ai

• LLM Innovation: Developed custom model wrappers and aggregators for Federated Learning applied to text-to-text LLMs, enabling training on resource-constrained CPU/GPU devices.
• Research & Federated Learning: Authored technical reports on Horizontal and Vertical Federated Learning for Predictive Maintenance, Object Detection, and Image Classification.
• Project Leadership: Led end-to-end development of a microscopy imagery project for collagen mutation in collaboration with the NIH and UCL, which improved model robustness and increased predictive performance by 8% over baseline benchmarks.
• Model Development: Created state-of-the-art training and parameter aggregation methods for Random Forests, Gradient Boosting, SVMs, and Logistic Regression.
• Cross-Industry Solutions: Executed FL projects for customer churn (Iberdrola, Telefonica), object detection (Leonardo, Indra), and cybersecurity threat detection (NetApp), improving the baseline benchmarks established by clients by 12% on average and allowing clients to showcase what they can do with Federated Learning.

Lead of the Awareness section, ONGAWA - MUCAM

Helped with the organization of the Awareness section

• Design the documents to explain people our mission
• Help to organize the exchange textil markets over different locations
• Gave several lectures on universities to explain our main purposes