Javier Saez Gallego, Developer in A Coruña, Spain
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Javier Saez Gallego

Verified Expert  in Engineering

Data Scientist and Developer

A Coruña, Spain
Toptal Member Since
February 19, 2019

Javier is a passionate data scientist who builds data-driven software products and helps organizations transform numbers into optimal decisions. With a multidisciplinary background, he has analyzed highly dimensional datasets and built machine learning models—starting from the research stage to the implementation phase. Javier is careful, very organized, meticulous about planning always focusing on delivering the right solution.


Computer Vision, Docker, Pandas, Data Science, Mathematical Modeling...
Docker, Pandas, Data Science, Mathematical Modeling, Machine Learning, Git...
Pandas, Data Science, Mathematical Modeling, Machine Learning, Optimization...




Preferred Environment

Linux, Docker, Git, Python, Azure, Amazon Web Services (AWS)

The most amazing...

...thing I've done was to develop an application used by sales engineers for the warranty calculation of wind turbines.

Work Experience

Data Scientist

2019 - PRESENT
  • Developed deep learning models that monitor forestry areas using satellite images. The final goal is to monitor forest conditions and to calculate carbon stocks in real-time.
  • Implemented a back end that performs ETL, machine learning modeling, and serves the results via an API.
  • Visualized GIS results via Mapbox.
  • Trained and deployed machine learning models in AWS.
Technologies: Computer Vision, Docker, Pandas, Data Science, Mathematical Modeling, Machine Learning, Mapbox, TensorFlow, Python, PyTorch

Data Scientist Consultant

2020 - 2020
  • Identified potential business cases where machine learning models could bring value to the clients.
  • Participated in the research, development, and implementation of predictive maintenance models of ATMs for one of the biggest banks in Europe.
  • Developed dashboards and interactive graphs.
Technologies: Docker, Pandas, Data Science, Mathematical Modeling, Machine Learning, Git, Dash, Python, PyTorch

Data Scientist

2019 - 2019
  • Created business value with available data from existing clients, focusing on data-driven action.
  • Implemented predictive maintenance of factory areas and optimal scheduling of hydroelectrical power plants.
  • Taught my colleagues the principles of data science and machine learning.
Technologies: Pandas, Data Science, Mathematical Modeling, Machine Learning, Optimization, SQL, Python

Data Scientist

2016 - 2018
Siemens Gamesa
  • Developed a framework consisting of a database, an app, and a dashboard in order to let users predict the performance of wind turbines.
  • Analyzed data from measurements and detected possible misalignments with the expected behavior.
  • Extracted meaningful information from multi-dimensional datasets.
  • Communicated the results and statistical terms to electrical engineers and sales officers in a clean and concise manner.
  • Read, studied, and kept up to date with current ISO standards.
  • Quantified the risk of different warranty strategies.
Technologies: Pandas, Data Science, Mathematical Modeling, Machine Learning, SQL, MATLAB, RStudio Shiny, R

PhD Candidate

2013 - 2016
Technical University of Denmark
  • Developed models for decision making under uncertainty, based on stochastic optimization techniques.
  • Built-up a new type of forecasting modeling framework that was based on inverse optimization techniques and machine learning principles.
  • Extensively used R for data processing and GAMS-CPLEX for building optimization models.
  • Used a cloud computing framework for parallelizing the calculations.
  • Presented the research topics and results in international conferences in Lisbon, Glasgow, and Philadelphia.
  • Published four articles in well-ranked journals.
  • Worked as a visiting scholar at the University of California for four months.
Technologies: Data Science, Mathematical Modeling, Operations Research, Machine Learning, R

Squirrel Problem

The project answers the following simple question: Can a squirrel cross from north to south Spain without touching the ground?

The answer is: obviously not. But, another question arises, and this one is not so easy to answer: If a squirrel had to go from the north of Spain to the south, touching the ground as little as possible: Which way would it follow?

The answer is not trivial, and it is answered here!

The project consisted of the following steps:
1. Download data from the internet. The data consists of GIS information to determine whether a piece of land is a forest, a river, a man-made construction, and so on.
2. Create a path matrix. The squirrel takes one step at a time. In terms of a raster map, this means we can reach each pixel only from adjacent pixels. Think of it as a huge spare matrix.
3. Optimize the path. Calculate the shortest path from the top of Spain to the bottom. The cost of each segment is the pixel value so forest costs nothing to move across and anything related to water is impossible to cross.
4. Visualize the solution as an interactive map.

Notification Bundler

A Python package for bundling notifications in event streams. The goal is to minimize the number of notifications sent to users and to not send more than four notifications per day.

Avatar Generation with AI

A full-stack application developed from scratch to populate any app or design with beautiful customized avatars generated with AI. Thousands of avatars are available for many pre-existing styles, and the possibility to generate your own. Includes a Python-based API deployed to a Kubernetes cluster.


I created a full-stack application that retrieves historical weather data from Open-Meteo and generates straightforward graphs illustrating climate changes worldwide. It leverages Google location services to suggest places for users. This project was built using Nuxt, Tailwind, and FastAPI and deployed on a Kubernetes cluster. "ShowYourLocalStripes" aims to demonstrate climate warming in a user-friendly way, drawing inspiration from Ed Hawkins' original #showyourstripes movement, with a focus on local warming trends.

Bird Audio Recognition

I designed a deep-learning model and implemented a back-end system for avian species classification using audio data. The model exhibits a distinctive capability to discriminate between bird songs and calls within the same species. The back-end infrastructure has been successfully deployed on Amazon Web Services (AWS) and processes over 500 requests per hour during peak times.
2010 - 2012

Master's Degree in Mathematical Modeling

Technical University of Denmark - Copenhagen, Denmark

2009 - 2011

Master's Degree in Statistics and Operations Research

University of Valladolid - Valladolid, Spain

2006 - 2009

Bachelor's Degree in Statistics and Operations Research

University of Valladolid - Valladolid, Spain


Deep Learning

Deeplearning.ai via Coursera


Node.js, Pandas, Plotly.js, PyTorch, Keras, Ggplot2, TensorFlow, React, Vue


Git, MATLAB, Plotly, CPLEX, Dplyr


Spring, Flask, RStudio Shiny


DevOps, Data Science


Java, Python, R, JavaScript, SQL, GAMS


Amazon S3 (AWS S3), PostgreSQL


Linux, Docker, Kubernetes, Amazon Web Services (AWS), Jupyter Notebook, Mapbox, Amazon EC2, Azure, AWS IoT


Software Development, Machine Learning, Data Analysis, Dashboard Design, Operations Research, Mathematical Modeling, Computer Vision, Algorithms, Artificial Intelligence (AI), Optimization, Dash, FastAPI, Audio Processing, APIs

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