Federico Albanese, Developer in Buenos Aires, Argentina
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Federico Albanese

Verified Expert  in Engineering

Predictive Modeling Developer

Buenos Aires, Argentina
Toptal Member Since
January 9, 2019

Federico is a developer and data scientist who has worked at Facebook, where he made machine learning model predictions. He is a Python expert and a university lecturer. His Ph.D. research pertains to machine learning. He can continuously learn and implement state-of-the-art algorithms during this process and become a better data scientist each day.


University of Buenos Aires
XGBoost, Keras, TensorFlow, Scikit-learn, MATLAB, R, Python
Python, Machine Learning, SQL, C++, Unsupervised Learning, Statistics...
Python, Machine Learning, Bayesian Statistics, SQL, Data Visualization, Git...




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.

Work Experience

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: XGBoost, Keras, TensorFlow, Scikit-learn, MATLAB, R, Python

PhD. Software Engineer Intern

2021 - 2021
  • I proposed a lookalike model to generate expanded audiences for Facebook and Instagram ads, using user embeddings and graph clustering.
  • I implemented a lookalike model to generate expanded audiences in Python.
  • I evaluated my proposed machine learning method, and the results showed an improvement in the precision, recall, and conversion score on synthetic and real data.
Technologies: Python, Machine Learning, SQL, C++, Unsupervised Learning, Statistics, Predictive Modeling, Data Visualization, Git, Python 3, Pandas

PhD. Software Engineer Intern

2020 - 2020
  • Experimented with and benchmarked uncertainty estimation methods for ad ranking models of Facebook.
  • Implemented deep learning models and analyzed more than 20 terabytes of data.
  • Improved the normalized entropy score for the ads matching task by 1.6%.
  • Designed and develop Bayesian deep learning models.
Technologies: Python, Machine Learning, Bayesian Statistics, SQL, Data Visualization, Git, Python 3, Pandas

Machine Learning Team Leader

2018 - 2019
  • Developed a forecasting algorithm that predicts the performance of NFL players.
  • Managed a group of four software engineers and data scientists.
  • Used web scrapping tools to automatically download data from different types of websites.
  • Outperformed the results of the state-of-the-art techniques by reducing the MSE from 8.541 to 5.576.
Technologies: Keras, TensorFlow, Scikit-learn, R, Python, Data Visualization, Python 3, Pandas

Invited Professor

2018 - 2019
Digital House
  • Dictated theoretical and applied lectures with code examples.
  • I used TensorFlow, Python, and Keras during the lessons.
  • The syllabus included the following topics: topic detection, dimensional reduction, embeddings, time series analysis, sentiment analysis, and text analysis/text mining.
Technologies: Keras, Matplotlib, TensorFlow, Scikit-learn, Python, Statistics, Data Visualization, Pandas

Data Scientist

2016 - 2017
Hexagon Consulting
  • Implemented different machine learning forecasting models using financial datasets.
  • Designed and implemented a recommendation system that used text analysis (natural language processing, topic detection, sentiment analysis, and word embedding).
  • Created and developed software that statistically calculates a personal index of inflation based on a big economic and financial database.
Technologies: JavaScript, Predictive Modeling, Scikit-learn, Python, Data Science, Data Visualization, Python 3
2018 - 2022

Ph.D. Candidate in Computer Science and Machine Learning

Buenos Aires University - Buenos Aires, Argentina

2011 - 2017

Licenciatura (Equivalent to a Bachelor + Master Degree) in Physics

Buenos Aires University - Buenos Aires, Argentina


Scikit-learn, PyTorch, Pandas, Keras, Matplotlib, XGBoost, TensorFlow


MATLAB, Jupyter, Spyder, Git


Python 3, Python, JavaScript, R, SQL, C++


Data Science


Linux, Windows


Neural Networks, Text Mining, Natural Language Processing (NLP), Embedded Software, Word2Vec, Regression Modeling, Predictive Modeling, Data Visualization, Statistics, Data Analysis, Web Scraping, GPT, Generative Pre-trained Transformers (GPT), Generative Adversarial Networks (GANs), Machine Learning, Bayesian Statistics, Unsupervised Learning

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