Enrique Pardo García
Data Scientist + ML Engineer, Data Engineer, Computer Vision, NLP Specialist, and Deep Learning
Maritime Auditory Intelligence — Datathon 2026
End-to-end maritime ML platform for anomaly detection, risk classification, temporal forecasting, and geographic clustering of maritime incidents. Six specialised models trained on real AIS data. Awarded 2nd place at ITG & UDC Datathon 2026.
Pathological Fluid Segmentation in OCT
Ablation study on U-Net architectures for retinal fluid segmentation in Optical Coherence Tomography images. Compares encoder backbones, loss functions, and data augmentation strategies with rigorous cross-validation.
Road Segmentation from Aerial Images
Deep learning pipeline for automatic road network extraction from high-resolution satellite imagery. Encoder-decoder CNN with combined loss and sliding window inference for arbitrary-resolution inputs.
Object Recognition with Pixel-Level Masks
Computer Vision pipeline for multi-class object recognition using CNNs with pixel-level segmentation masks and data augmentation. Produces dense per-pixel class maps rather than bounding boxes.
Clinical Trial Information Retrieval System
NLP-based search engine for biomedical text combining lexical retrieval with neural semantic re-ranking. Lucene indexing and BM25 for first-stage retrieval, BioBERT for passage relevance scoring.
IoT Malware Detection with PySpark
Distributed big data analytics for cybersecurity. Apache Spark Structured Streaming processes network traffic in real time, Isolation Forest detects behavioural anomalies, and gradient boosting classifies malware families.
Maritime Visual Classification at Smartports
Multi-task deep learning for smart port operations — simultaneous ship type and docking status classification from port camera feeds, with ablation studies over backbone architectures and training strategies.
UptimeAI — Infrastructure Monitoring SaaS
Full-stack SaaS for monitoring websites, REST APIs and cron jobs 24/7. Real-time alerting via email, SMS, Slack and Telegram, public status pages, and Stripe billing — built with Next.js 14, Supabase, and Inngest.
Sparse Autoencoders for Mitotic Figure Representation (Bachelor Thesis)
Bachelor thesis on unsupervised sparse coding for H&E histology: a LISTA-based sparse autoencoder evaluated across six configurations (MSE/MAE/SSIM × two dictionary sizes) on the MIDOG dataset, analysing reconstruction quality and discriminative potential between mitotic and non-mitotic cells.
Enrique Pardo García
I'm a Data Scientist & Data Engineer from Palencia, Spain. I hold a degree in Data Science & Engineering from the Universidade da Coruña and I'm currently pursuing a Master's in Artificial Intelligence at Mondragon Unibertsitatea. I build scalable, data-driven systems across computer vision, NLP, distributed computing, and real-time analytics — transforming raw data into intelligent, production-ready outcomes.
My work spans deep learning for computer vision, natural language processing and information retrieval, distributed data engineering, and predictive modelling — from biomedical image segmentation and satellite road extraction to IoT anomaly detection and large-scale maritime ML. Recognised with 2nd place at Datathon 2026 (ITG & UDC). See my full stack or download my CV.
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Education & Certifications
Academic background and language certification underpinning my technical work.
BSc in Data Science & Engineering
Universidade da Coruña (UDC)
2022 – 2026
Master's Degree in Artificial Intelligence
Mondragon Unibertsitatea
Since 2026
Cambridge English C1 Advanced (CAE)
Cambridge Assessment English
2021
Work Experience
Hands-on data engineering work applying the same tools and practices behind my research projects.
Sep – Oct 2025
Data Engineer Intern
ENXENIO S.L. — Galicia, Spain
- Optimised Power BI dashboards and built enterprise reporting pipelines for financial data analysis.
- Ran data quality audits on PostgreSQL and implemented validation frameworks.
- Implemented Row-Level Security (RLS) policies for secure multi-tenant data access.
- Designed and maintained ETL processes over large-scale financial datasets with quality validation.
- Collaborated with cross-functional teams to deliver scalable, resilient data solutions.