I am a Senior Data Scientist & AI & Machine Learning Engineer with a strong academic background and hands-on experience delivering high-impact AI solutions in industry. I graduated from the elite dual degree in Mathematics and Computer Science at Complutense University of Madrid, and later completed a Master's in Artificial Intelligence Research at UNED.
Throughout my career, I have worked at leading organizations such as Boston Consulting Group and TomTom, designing and deploying end-to-end AI systems, from data engineering and experimentation to model development, evaluation, and production. My work spans deep learning, generative and agentic AI, optimization, forecasting, and intelligent data systems, always with a clear focus on creating measurable business impact.
I enjoy tackling complex problems and translating real-world challenges into data-driven solutions. I am known for my analytical rigor, technical depth, and adaptability, as well as my ability to collaborate effectively in diverse teams and communicate technical concepts clearly to non-technical stakeholders.
I am passionate about applied research and cutting-edge AI technologies, and I am eager to keep contributing to areas such as GenAI, agentic AI, MLOps, deep learning, and cloud-based AI systems.
Experience
Senior Data Scientist & AI & Machine Learning Engineer
TomTom
- Architected benchmarking frameworks using Kedro, Spark, and Azure to assess internal and competitor map quality across POIs, addresses, and ADAS data, supporting strategic client initiatives with multi-million-dollar impact.
- Built a confidence scoring model (PySpark, XGBoost) that reduced POI superfluousness metrics by 90%.
- Fine-tuned PyTorch LLMs for POI categorization using Hugging Face and MLflow on Databricks.
- Improved entity matching F1-score by +20% through graph-based clustering and advanced feature engineering.
- Developed multimodal agentic AI systems for ground-truth data generation and validation.
- Contributed to team learning initiatives and to the team’s software development guidelines.
Data Scientist & Machine Learning Engineer
Boston Consulting Group (BCG)
- Developed an optimization model for a petrochemical company in the Middle East, optimizing the end-to-end value chain and performing what-if analyses that generated an annual economic impact of +$10M.
- Implemented demand forecasting models with XGBoost across multiple clients, improving existing model performance by 20%.
- Built pricing forecasting models using Regression and Transformer architectures, improving prediction accuracy by +12%, and applied Reinforcement Learning to optimize pricing strategy with +8% margin uplift.
- Optimized shipping schedules using evolutionary algorithms, reducing delivery delays by 30% and improving fleet utilization by 22%.
- Developed a RAG-based industrial chatbot that helps internal users answer their queries by retrieving relevant company documentation, reducing manual query handling by 50%.
- Delivered data-driven insights to senior client stakeholders, informing strategic decisions across pricing, operations, and customer management.
Education
Master's in Artificial Intelligence Research
UNED
Completed 66 ECTS (vs. 60 standard), covering advanced topics in Deep Learning, NLP, Computer Vision, Reinforcement Learning, Generative Models, Metaheuristics, and Graph/Probabilistic Models.
Thesis: RL for hyperparameter control in population-based metaheuristics (SIMDA Research Group).
National admission rank: Top 2 in Spain (GPA 13.66/14)
Bachelor of Mathematics
Complutense University of Madrid
Relevant coursework: Problem Solving, AI, Advanced Analysis & Algebra & Geometry & Statistics, Optimization
Bachelor of Computer Science
Complutense University of Madrid
Relevant coursework: Object-Oriented Programming, AI, Database Management, Concurrency, Operating Systems
Articles
Agentic RAG and GraphRAG (2026)
Advanced retrieval-augmented generation techniques focused on agentic retrieval and graph-based approaches to improve reasoning and structured knowledge use in agents.
Retrieval-Augmented Generation (RAG) (2026)
How RAG combines external information retrieval with generative models to ground outputs in real data for better accuracy and relevance.
AI Agent Observability
Techniques for observing AI agents in production, including logging, metrics, traces, and debugging frameworks.
Building a Production MCP Server: Real-World Lessons from Exposing Spanish Government Data
Real-world insights and challenges from building a production MCP server to expose government data reliably.
How to Evaluate AI Agents: End-to-End Metrics, Tool Correctness, and Failure Modes
A comprehensive guide to evaluating AI agents, including full-pipeline performance metrics and detection of common failure modes.
Feedback Loops for AI Agents
How to design feedback mechanisms so AI agents improve over time with automated and human feedback signals.
Context Engineering for AI Agents (2026)
How to structure and optimize context for agent reasoning to improve quality and cost control.
Memory Engineering for AI Agents: How to Build Real Long-Term Memory
A systems-oriented exploration of real long-term memory design for agents that goes beyond raw context windows.
AI Agent Tools (2026)
Overview of tools and frameworks used for building and orchestrating AI agents in 2026.
Claude Code Tool Best Practices: CLI, Slash Commands, and MCP
A comparison of the different ways to interact with Claude Code tools and when to choose each interface.
Claude Code Best Practices
General best practices for working with Claude Code for reliability and efficiency.
Multi-Agent System Patterns: Designing Agentic Architectures
An architectural guide to multi-agent systems, covering coordination, execution, interaction, and deeper system design.
Prompt Engineering Basics (2026): A Practical Guide
Practical techniques for writing reliable prompts, treating prompts as clear specifications with structured constraints.
Advanced Prompt Engineering (2026)
Advanced prompt engineering techniques beyond the basics, covering complex patterns for reliable and high-quality LLM outputs.
Control Loops for Agentic AI: HITL & AITL Design Patterns
Patterns for control loops such as Human-in-the-Loop (HITL) and AI-in-the-Loop (AITL) in production-grade agent systems.
Hyperparameter Optimization with Optuna
Technical guide on using Optuna for hyperparameter tuning, including samplers and key design trade-offs.
Building a Practical Framework for Supervised Tabular ML
A practical end-to-end framework for supervised machine learning on tabular data, covering preprocessing, model selection, and evaluation.
Certifications
53 professional certifications from world-leading institutions
Machine Learning Specialization
Comprehensive specialization covering supervised learning, unsupervised learning, recommender systems, and reinforcement learning fundamentals.
VerifySupervised Machine Learning: Regression and Classification
Core foundations of supervised learning including linear regression, logistic regression, and gradient descent optimization.
VerifyAdvanced Learning Algorithms
Neural networks, decision trees, ensemble methods, and best practices for training and evaluating ML models.
VerifyUnsupervised Learning, Recommenders, Reinforcement Learning
Clustering, anomaly detection, collaborative filtering, and introduction to reinforcement learning concepts.
VerifyProbabilistic Graphical Models Specialization
Complete specialization in PGMs covering representation, inference, and learning of Bayesian and Markov networks.
VerifyProbabilistic Graphical Models 1: Representation
Bayesian networks, Markov random fields, and template models for representing complex probability distributions.
VerifyProbabilistic Graphical Models 2: Inference
Exact and approximate inference algorithms for probabilistic graphical models, including message passing and sampling.
VerifyProbabilistic Graphical Models 3: Learning
Parameter and structure learning in probabilistic graphical models from observed and partially observed data.
VerifyDeep Learning Specialization
Comprehensive deep learning specialization covering neural networks, optimization, CNNs, sequence models, and structuring ML projects.
VerifyNeural Networks and Deep Learning
Foundations of neural networks, forward/backward propagation, and building deep neural network architectures.
VerifyImproving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Techniques for improving neural network performance including batch normalization, dropout, and advanced optimizers.
VerifyStructuring Machine Learning Projects
Best practices for structuring ML projects, error analysis, and strategies for handling mismatched data distributions.
VerifyConvolutional Neural Networks
CNN architectures, object detection, face recognition, and neural style transfer using convolutional neural networks.
VerifySequence Models
RNNs, LSTMs, GRUs, attention mechanisms, and Transformer architectures for sequence-to-sequence modeling.
VerifyTensorFlow: Advanced Techniques Specialization
Advanced TensorFlow specialization covering custom models, distributed training, computer vision, and generative deep learning.
VerifyCustom Models, Layers, and Loss Functions with TensorFlow
Building custom layers, loss functions, and model architectures using TensorFlow's flexible APIs.
VerifyCustom and Distributed Training with TensorFlow
Custom training loops, gradient tape, and distributed training strategies for large-scale deep learning.
VerifyAdvanced Computer Vision with TensorFlow
Object detection, image segmentation, and model interpretability using advanced TensorFlow computer vision techniques.
VerifyGenerative Deep Learning with TensorFlow
Variational autoencoders, GANs, and neural style transfer for generating new content with deep learning.
VerifyIntroduction to Data Engineering
Foundations of data engineering including data lifecycle, architecture patterns, and modern data stack components.
VerifySource Systems, Data Ingestion, and Pipelines
Designing data ingestion pipelines, working with source systems, and building reliable data workflows.
VerifyData Storage and Queries
Data storage solutions, query optimization, and database technologies for modern data engineering.
VerifyMLOps | Machine Learning Operations Specialization
End-to-end MLOps specialization covering DevOps, DataOps, MLOps tools, and cloud ML platforms for production ML systems.
VerifyDevOps, DataOps, MLOps
Principles and practices of DevOps, DataOps, and MLOps for building robust and automated data and ML pipelines.
VerifyMLOps Tools: MLflow and Hugging Face
Hands-on MLOps with MLflow for experiment tracking and model registry, and Hugging Face for model deployment.
VerifyMLOps Platforms: Amazon SageMaker and Azure ML
Deploying and managing ML models on AWS SageMaker and Azure ML cloud platforms for production use.
VerifyVirtualization, Docker, and Kubernetes for Data Engineering
Container orchestration with Docker and Kubernetes for scalable data engineering infrastructure.
VerifyAdvanced Data Engineering
Advanced techniques for building scalable, reliable, and efficient data engineering systems.
VerifyAWS Fundamentals Specialization
Core AWS services and cloud architecture fundamentals including compute, storage, networking, and security.
VerifyAWS Cloud Technical Essentials
Foundational AWS cloud services, infrastructure, and best practices for cloud-based application deployment.
VerifyMigrating to the AWS Cloud
Cloud migration strategies and best practices for moving workloads to AWS infrastructure.
VerifyArchitecting Solutions on AWS
Designing scalable, resilient, and cost-efficient solutions using AWS services and architectural patterns.
VerifyAWS Cloud Solutions Architect
Advanced cloud architecture design principles for highly available and fault-tolerant systems on AWS.
VerifyIntroduction to Designing Data Lakes on AWS
Data lake design patterns using AWS services for scalable and cost-effective data storage and analytics.
VerifyGenerative AI with Large Language Models
LLM lifecycle from pre-training through fine-tuning and deployment, including RLHF and prompt engineering.
VerifyGenerative AI for Software Developers Specialization
Specialization on applying generative AI in software development, from prompt engineering to building AI-powered applications.
VerifyGenerative AI: Introduction and Applications
Introduction to generative AI concepts, models, and real-world applications across industries.
VerifyGenerative AI: Prompt Engineering Basics
Fundamentals of prompt engineering for effective interaction with large language models.
VerifyGenerative AI: Elevate your Software Development Career
Leveraging generative AI tools and techniques to enhance software development productivity and code quality.
VerifyIntroduction to Big Data with Spark and Hadoop
Big data fundamentals using Apache Spark and Hadoop for distributed data processing and analytics.
VerifyMachine Learning with Apache Spark
Building scalable machine learning pipelines using Apache Spark MLlib for classification, regression, and clustering.
VerifyIntroduction to NoSQL Databases
NoSQL database concepts, types (document, key-value, graph, column), and use cases for modern applications.
VerifyReinforcement Learning Specialization
Complete RL specialization from fundamentals through function approximation to building a complete RL system.
VerifyFundamentals of Reinforcement Learning
Core RL concepts including MDPs, dynamic programming, and the exploration-exploitation trade-off.
VerifySample-based Learning Methods
Monte Carlo methods, temporal difference learning, and planning with sample-based approaches in RL.
VerifyPrediction and Control with Function Approximation
Function approximation methods in RL including linear and neural network-based value function approximation.
VerifyA Complete Reinforcement Learning System (Capstone)
Capstone project building a complete RL system integrating all concepts from the specialization.
VerifyAI Agents and Agentic AI with Python & Generative AI
Building AI agents with Python, covering agentic AI patterns, tool use, and generative AI integration.
VerifyThe Path to Insights: Data Models and Pipelines
Data modeling and pipeline design for transforming raw data into actionable business insights.
VerifySelecting the Right LLM with Hugging Face
Evaluating and selecting the right large language model for specific use cases using Hugging Face tools.
VerifyAdvanced Relational Database and SQL
Advanced SQL queries, database optimization, and relational database design for data engineering.
VerifySpark, Hadoop, and Snowflake for Data Engineering
Distributed data processing and modern data warehousing with Spark, Hadoop, and Snowflake.
VerifyDatabricks to Local LLMs
Deploying and running LLMs from Databricks to local environments for inference and fine-tuning.
Verify