Pau Labarta Bajo, Mathematical Modeling Developer in Belgrade, Serbia
Pau Labarta Bajo

Mathematical Modeling Developer in Belgrade, Serbia

Member since January 23, 2019
Pau is a data scientist and ML engineer with over 8 years of experience. He has a passion for building ML-based solutions, from development to deployment. He loves transforming an idea into a model, and a model into an API/product. Pau has worked on different problems: financial derivative pricing, digital marketing analytics, deep learning for art generation, or demand prediction for on-line shopping. His background is in pure mathematics and he has strong coding skills in Python.
Pau is now available for hire




Belgrade, Serbia



Preferred Environment

Mac, PyCharm, AWS

The most amazing...

...model I've built is a generative neural network that creates realistic profile pictures for football players in a mobile game.


  • Python/ML Developer

    2020 - PRESENT
    15kay (via Toptal)
    • Supported the development of a scientific Python package in the medical field.
    • Researched applicability of the package inside the open-source ML/AI ecosystem.
    • Created tutorial notebooks to showcase potential uses of the tool.
    Technologies: Python, Jupyter, TensorFlow
  • ML engineer

    2020 - 2020
    Lola Market - Freelance
    • Developed, deployed, and maintained a Machine Learning model to improve the efficiency of the shoppers' fleet.
    • Bootstrapped the first data-warehouse and reporting layer in the company (Amazon Redshift + Amazon DMS + Tableau).
    • Developed several dashboards to help the client improve its fleet management efficiency.
    Technologies: Python, AWS
  • Machine learning engineer

    2020 - 2020
    Toptal client
    • Performed a statistical analysis of financial markets using explainable Machine Learning techniques.
    • Wrote a Python package to ensure the in-house reproducibility of each step of the analysis (data processing, data validation, data visualization, model construction, model validation, and model explanation).
    • Benchmarked a range of ML solutions to enhance model accuracy and explainability.
    Technologies: Python, Explainable ML (Shap), Scikit-learn
  • Data Scientist

    2019 - 2020
    Goguru (via Toptal)
    • Advised the client on how to create their first data analytics stack.
    • Developed components of the ETL with Python and SQL, and the whole frontend with Tableau.
    • Developed and deployed the first impactful ML predictive model for the company.
    Technologies: Amazon Redshift, Amazon DMS, Python, Tableau
  • Data Scientist

    2019 - 2020
    Cyngn (via Toptal)
    • Created, updated, and maintained the front-end dashboards of the data analytics stack at Cyngn.
    • Developed quick visualization prototypes in Tableau and deployed them into dashboards accessible to the engineering team.
    • Developed components of the internal ETL tool in Python and SQL.
    • Assisted our back-end engineer to integrate frontend and backend of the stack inside Amazon Redshift.
    Technologies: Amazon Redshift, Tableau, SQL
  • Mathematical C++ developer (genetics project)

    2019 - 2019
    • Reviewed and documented the proprietary algorithm that performs base calling.
    Technologies: OpenCV, C++
  • ML engineer

    2019 - 2019
    Toptal client
    • Developed statistical and machine learning models to understand the market valuation of financial institutions.
    • Created a reproducible pipeline for data science, from data transformation to hyper-parameter model tuning.
    • Placed a special emphasis on model interpretability.
    Technologies: Python, Jupyter, Scikit-learn
  • Data Scientist and Machine Learning Engineer

    2016 - 2019
    • Created a neural network that is able to generate football player faces and that is used in one of the company‚Äôs games.
    • Improved the algorithms used to matchmake players in the game.
    • Worked with a player support team to automate the process of tagging player complaints using NLP techniques.
    • Developed a model used to predict the lifetime value of the users.
    • Implemented simulations of the game economy in order to balance it and ensure a good player experience and good monetization.
    Technologies: Python, Hadoop, Impala, Tableau
  • Quantitative Risk Analyst

    2012 - 2016
    Erste Group Bank
    • Implemented and validated in Matlab and Python all models used by Erste Group Bank to price and hedge interest rate derivatives.
    • Wrote exhaustive documentation for each validated model in order to present it to the European Central Bank.
    • Proposed and implemented improvements to the methodology used to estimate the credit market risk of the banking and trading books.
    • Backtested the performance of different Value At Risk models in order to propose improvements to the methodology used by the bank.
    • Mentored junior quantitative risk analysts.
    Technologies: Python, Matlab, Calypso


  • Realistic Human Face Generator for Mobile App Golden Boot 2019 (Development)

    The problem I wanted to solve was to fully automate the process of generating profile images of football players for several of the company's games. The system is used in the mobile game Golden Boot 2019, available in both iOS and Android, and with over 1 million installs since its release.

    I built a pipeline of three models, each applied sequentially. First, a cutting-edge GAN network, retrained to my own dataset, that generates realistically looking football player faces. Second, a logistic classifier built from the last layer of a VGG network, to classify the output of the GAN into "good" faces and "bad" faces ensuring that only images of sufficient quality are displayed to the user. Third, another logistic regression on top of the last layer of a VGG net to classify the face according to its ethnicity. This last step was necessary in order to have control over the correlation between football player nationality and his physical appearance.

  • Adversarial Machine Learning: How to Attack and Defend ML Models (Publication)
    The increasing accuracy of machine learning systems has resulted in a flood of applications using them. As machine learning models matured and improved, so did ways of attacking them. In this article, Toptal Python Developer Pau Labarta Bajo examines the world of adversarial machine learning, explains how ML models can be attacked, and what you can do to safeguard them against attack.


  • Languages

    Python, SQL
  • Frameworks

  • Libraries/APIs

    Scikit-learn, Keras, TensorFlow, OpenCV, PySpark
  • Tools

  • Paradigms

    Data Science
  • Other

    Machine Learning, Natural Language Processing (NLP), Statistics, Statistical Modeling, Computer Vision, Quantitative Finance, Pyspark, Mathematical Modeling, Time Series Analysis, Deep Learning, Genomics, Custom BERT
  • Platforms

    Google Cloud Platform (GCP), Amazon Web Services (AWS)
  • Storage



  • Master's degree in Quantitative Economics
    2011 - 2012
    Ca'Foscari University Venice - Venice, Italy
  • Master's degree in Quantitative Economics
    2010 - 2011
    University of Bielefeld - Bielefeld, Germany
  • Master's degree in Mathematics
    2005 - 2010
    Polytechnic University of Catalonia - Barcelona, Spain


  • Participant in the 46th International Mathematical Olympiad
    JULY 2005 - PRESENT
    International Mathematical Olympiad

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