Hi, I'm Javier Fernández

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Self-driven, quick starter, passionate programmer with a curious mind who enjoys developing and deploying AI models.

About

I am a an Industrial Engineering Grad Student working as a Data Scientist over the last few years. I enjoy problem-solving and developing complex applications that solve real-world problems impacting millions of users.

  • Programming languages: Python, C, C++, C#, SQL
  • Libraries and frameworks: PyTorch, Keras, Scikit-learn, Pandas, Spark, Hugging Face, Flask, MLServer
  • Databases: Snowflake, MongoDB
  • Environments: Conda, Pipenv, Poetry, Visual Studio Code, Git
  • Operating systems: Mac OS X, Windows, Ubuntu
  • Other: Docker, PowerBI, Unity, Latex, Pack Office

Looking for an opportunity to work in a challenging position related to a Data Science or ML Engineer position, which provides professional development, interesting experiences and personal growth.

Experience

Data Scientist and ML Engineer
  • E-commerce smart warehouse assistant for monitoring.
    • Integration of AI-based recommendations.
    • ML Server API architecture and development.
    • CI/CD pipeline with GitHub action.
  • Tools: Python, PyTorch, ML Server, Databricks, Docker, Azure DevOps, GitHub CI/CD
April 2022 - Currently | Madrid, Spain
AI research assistant
  • Cross-subject EEG-based emotion recognition.
  • Emotion-driven interactive storytelling.
  • Optimizing lockdown policies for COVID-19.
  • Studying the human perception perception-based color.
  • Tools: Neuroscience, Affective computing, ALife, Interactive storytelling
April 2020 - Currently | Kyoto, Japan
Data Scientist
  • E-commerce smart warehouse assistant for monitoring.
  • Personalized academic research paper recommendation system.
  • Regression for number of pallets estimation.
  • Unsupervised image anomaly detection.
  • Tools: Python, SQL, Snowflake, Databricks, Data Factory, Azure DevOps, PowerBI, NLP, Forecasting
April 2021 - April 2022 | Madrid, Spain
Data Scientist
  • Supervised ECG-based arrhythmia detection.
  • Unsupervised anomaly detection for a turbine and a paper machine.
  • SOTA models for EEG-based emotion classification systems.
  • Combining EEG signals and deep learning for expert classification.
  • Tools: Python, PyTorch, 1D CNN, 2D CNN, LSTM, XGBoost
April 2019 - April 2021 | Tokyo, Japan

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Education

Keio University

Tokyo, Japan

Degree: Master of Science in Engineering
2017-2019

    Relevant Courseworks:

    • Machine Learning
    • Human-Robot Interaction
    • Computer Vision
    • Natural Language Processing

Universidad Politécnica de Madrid (UPM)

Madrid, Spain

Degree: Bachelor and Master of Industrial Engineering
2012-2016 Bachelor, 2016-2019 Master

    Relevant Courseworks:

    • Robotics
    • Automation
    • Electronics
    • Mechanics of Fluids
    • Heat
    • Materials
    • Management

Contact