Daniel Huencho
Daniel Huencho
AI Engineer | MSc AI for Sustainable Development @ UCL
With over seven years of experience delivering impactful data-driven solutions in finance and transportation, I specialize in designing AI systems for predictive maintenance, anomaly detection, and generative AI applications. Currently pursuing my Master's at UCL, passionate about leveraging data and technology to create measurable societal value.
About
With over seven years of experience delivering impactful data-driven solutions in finance and transportation, I specialize in designing AI systems for predictive maintenance, anomaly detection, and generative AI applications. Currently pursuing my MSc in AI for Sustainable Development at University College London (UCL), I am passionate about leveraging data and technology to innovate complex systems and create measurable societal value. My research interests lie at the intersection of deep learning and earth observation, particularly using methods like Deep Kernel Learning (DKL) for disaster risk assessment and urban resilience.
Most recently, I served as Head of Data Science at Metro de Santiago, where I led a team of 5 data scientists and data engineers to design and deploy AI-driven solutions, architecting an end-to-end big data pipeline for high-frequency SCADA energy data and implementing generative AI agents for operations. Prior to that, I worked as an Analytics Translator at BCI and a Senior Risk Management Analyst at Banco de Chile, bridging the gap between business strategy and technical implementation.
Technical Expertise
Programming Languages
Python SQL R C++
Machine Learning & Deep Learning
PyTorch Scikit-learn Deep Learning TensorFlow Ensemble Models Generative AI MLlib
Data Engineering & Cloud
Apache Spark ETL Pipelines Databricks AWS (EC2, S3, Glue, Athena) Docker
Statistical Methods
Bayesian Statistics Time Series Analysis Generalized Linear Models A/B Testing Survival Analysis
Highlights
2025 – Present
MSc AI for Sustainable Development
University College London (UCL)
Research focus on deep learning methods for building damage assessment using satellite imagery. Exploring Deep Kernel Learning and Graph Neural Networks for disaster response.
2023 – 2025
Head of Data Science
Led a team of 5 data scientists and data engineers to design and deploy AI-driven solutions. Architected end-to-end big data pipeline for high-frequency SCADA energy data. Implemented generative AI agents for Operations Control Center and Maintenance tasks.
2022 – 2023
Analytics Translator
Bridged technical data science expertise with business operations. Led data-driven profitability initiative using behavioral and financial clustering. Designed optimized customer journey with A/B testing validation.