Federico Albanese, Data Visualization Developer in Buenos Aires, Argentina
Federico Albanese

Data Visualization Developer in Buenos Aires, Argentina

Member since October 6, 2018
Federico is currently pursuing a PhD in computer science, studying and designing new machine learning techniques. During this process, he's able to continuously learn and implement state-of-the-art algorithms and become a better data scientist each day. In the last few years, Federico has also worked at a financial consulting company, analyzing transactional data and making model predictions.
Federico is now available for hire




Buenos Aires, Argentina



Preferred Environment

Git, Spyder, Jupyter, Windows, Linux

The most amazing...

...prediction model I've coded outperformed the state of the art models in that area by 15%. This model was also fast and easy to interpret.


  • Machine Learning Team Leader

    2018 - PRESENT
    • Developed a forecasting algorithm that predicts the performance of NFL players. The results outperform state of the arte techniques by reducing the MSE from 8.541 to 5.576.
    Technologies: Keras, TensorFlow, Scikit-learn, R, Python
  • Invited Professor

    2018 - PRESENT
    Digital House
    • Dictated theoretical and applied lectures on the following topics: topic detection, conditionality reduction, embedding, time series analysis, embeddings, sentiment analysis, and text analysis/text mining. I used Tensorflow, Python, and keras during the lessons.
    Technologies: Keras, Seaborn, Matplotlib, Bokeh, TensorFlow, Scikit-learn, Python
  • Research intern

    2017 - PRESENT
    University of Buenos Aires
    • Analyzed the texts of news using natural language processing techniques. In particular, recursive deep models for semantic compositionality over sentiment treebank in order to detect the sentiment of a sentence, dimensional reduction algorithms and topic detection methods were used with the intention of characterizing the mass media bias during presidential elections.
    • Focused my study on developing better machine learning techniques which efficiently uses the information of a node and its neighbours. In addition, This new semi supervised methodology will be validated using graphs of biological and social origin.
    Technologies: CatBoost, XGBoost, Keras, TensorFlow, Scikit-learn, MATLAB, R, Python
  • Data Scientist

    2016 - 2017
    Hexagon Consulting
    • Implemented different predictive models in order to describe the future financial behavior of bank clients using Python.
    • Designed and implemented a recommendation system that uses text reviews in order to recommend a movie using text analysis (topic detection, sentiment analysis, and word embedding).
    • Created and develop a software which statistically calculates a personal index of inflation based on a big economic and financial database.
    Technologies: Bokeh, Seaborn, D3.js, JavaScript, Predictive Modeling, Scikit-learn, Python



  • Languages

    Python 3, Python, JavaScript, R, SQL
  • Libraries/APIs

    Scikit-learn, Keras, D3.js, Matplotlib, XGBoost, CatBoost, TensorFlow
  • Tools

    MATLAB, Jupyter, Spyder, Git, Seaborn
  • Other

    Neural Networks, Text Mining, Natural Language Processing (NLP), Embedded Software, Word2Vec, Regression Modeling, Predictive Modeling, Data Visualization, Generative Adversarial Networks (GANs), Bokeh
  • Platforms

    Linux, Windows


  • Ph.D. Candidate in Computer Science and Machine Learning
    2018 - 2021
    Buenos Aires University - Buenos Aires, Argentina
  • Licenciatura (Equivalent to a Bachelor + Master Degree) in Physics
    2011 - 2017
    Buenos Aires University - Buenos Aires, Argentina

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