Federico Albanese, Predictive Modeling Developer in Buenos Aires, Argentina
Federico Albanese

Predictive Modeling Developer in Buenos Aires, Argentina

Member since October 6, 2018
Frederico is currently pursuing a Ph.D. 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 with each day. In the last few years, Frederico has also worked at a financial consulting company analyzing transactional data and making model predictions.
Federico is now available for hire



  • Python 3, 5 years
  • Predictive Modeling, 5 years
  • Data Visualization, 5 years


Buenos Aires, Argentina



Preferred Environment

Linux, Windows, Jupyter, Spyder, Git

The most amazing...

...prediction model I had 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: Python, R, Sklearn, Tensorflow, Keras
  • Invited Profesor

    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: Python, Sklearn, Tesnorflow, Bokeh, Matplotlib, Seaborn, Keras
  • 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: Python, R, Matlab, Sklearn, Tesnorflow, Keras, Xgboost, Catboost, Lightboost
  • 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: Python, Sklearn, Predictive models, JavaScript, D3, Seaborn, Bokeh



  • Languages

    Python 3, R, SQL
  • Libraries/APIs

    Sklearn, Keras, TensorFlow
  • Tools

  • Other

    Neural Networks, Text Mining, Natural Language Processing (NLP), Embedded Software, Word2vec, Regression Models, Predictive Modeling, Data Visualization, Generative Adversarial Networks (GANs)


  • 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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