Pau Labarta Bajo, Mathematical Modeling Developer in Barcelona, Spain
Pau Labarta Bajo

Mathematical Modeling Developer in Barcelona, Spain

Member since April 24, 2019
Pau is a data scientist and ML engineer with over eight 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 or product. Pau has worked on different problems: financial derivative pricing, digital marketing analytics, deep learning for art generation, or demand prediction for online shopping. His background is in pure mathematics, and he has strong coding skills in Python.
Pau is now available for hire




Barcelona, Spain



Preferred Environment

Amazon Web Services (AWS), AWS, PyCharm, MacOS

The most amazing...

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


  • Machine Learning Engineer

    2021 - PRESENT
    • Developed ML-based bidding bot to acquire leads more cost-effectively.
    • Developed a churn prediction model to anticipate policy churn and increase customer retention.
    • Built a simulation engine to optimize key parameters for daily operations.
    Technologies: Google Cloud Platform (GCP), Python
  • Time Series ML Engineer

    2021 - 2021
    Cogsy Limited
    • Validated and improved the forecasting methodology that powers Cogsy's app.
    • Built an in-house Python package for fast experimentation, leveraging Amazon Forecast AutoML, and custom feature engineering.
    • Developed ad-hoc predictive models for several of Cogsy's clients.
    Technologies: Amazon Web Services (AWS), Python 3, DeepAR
  • Data Engineer

    2020 - 2021
    Speakeasy Labs
    • Increased the robustness of the marketing analytics pipeline.
    • Helped define and implement an event tracking system adapted to the new iOS 14 tracking restrictions.
    • Advised the client on specific low-level details related to
    Technologies: REST APIs, Segment
  • Machine Learning Engineer

    2020 - 2020
    Lola Market - Freelance
    • Developed, deployed, and maintained an ML model to improve the efficiency of the shoppers' fleet.
    • Bootstrapped the company's first data warehouse and reporting layer, including Amazon Redshift, Amazon Database Migration Service (DMS), and Tableau.
    • Developed several dashboards to help the client improve its fleet management efficiency.
    Technologies: Amazon Web Services (AWS), AWS, Python, Scikit-learn
  • Machine Learning Engineer | Statistician

    2020 - 2020
    Toptal Client
    • Analyzed financial market valuations in the Gulf region using explainable machine learning.
    • Wrote a Python package to ensure the in-house reproducibility of each step of the analysis, including data processing, data validation, data visualization, model construction, model validation, and model explanation.
    • Benchmarked a range of ML solutions and fine-tuned them to enhance model accuracy and explainability.
    Technologies: Shapely, Statistics, Scikit-learn, Machine Learning, Python
  • Explainable AI Engineer

    2020 - 2020
    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 and AI ecosystem.
    • Created tutorial notebooks to showcase potential uses of the package.
    Technologies: Explainable Artificial Intelligence (XAI), TensorFlow, Jupyter, Python
  • Data Scientist | Data Engineer

    2019 - 2020
    Goguru Consulting
    • Deployed the client's first data warehouse and data reporting system.
    • Developed components of the analytics stack from scratch using Python, SQL, AWS Redshift, and Tableau Online.
    • Developed a machine learning model to increase the operational efficiency of Lola Market, a client of Goguru. Lola Market offers its customers the possibility to buy groceries online and have them delivered to their homes in a matter of hours.
    Technologies: Random Forests, Scikit-learn, Amazon Web Services (AWS), Tableau, Python, AWS Database Migration Service, AWS, Redshift
  • Data Visualization | Data Engineer

    2019 - 2020
    • 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.
    • Helped back-end engineers integrate front end and back end of the stack inside Amazon Redshift.
    Technologies: Amazon Web Services (AWS), SQL, Tableau, AWS, Redshift
  • Mathematical C++ Developer (Genetics Project)

    2019 - 2019
    • Reviewed and documented the proprietary algorithm that performs base calling.
    • Advised the client on how to improve the current algorithm.
    • Debugged the code and proposed improvements to increase accuracy.
    Technologies: C++, OpenCV
  • Machine Learning 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: Scikit-learn, Jupyter, Python
  • Data Scientist | Machine Learning Engineer

    2016 - 2019
    • Created a neural network model to generate football player faces in a scalable way. The outputs from this model are used in one of the company's games.
    • Designed matchmaking algorithms in the Top Eleven game, a soccer manager simulation with over 200 million users worldwide using game theory and Monte Carlo techniques.
    • Worked with the internal customer support team to automate the process of tagging player complaints using NLP techniques.
    • Developed a predictive model to estimate the marketing campaigns' ROAS (return on ad spend).
    • Managed two junior data scientists responsible for business intelligence and game system design.
    Technologies: Scikit-learn, Tableau, Impala, Hadoop, Python, Data Analyst
  • 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 to present 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 to propose improvements to the methodology used by the bank.
    • Mentored junior quantitative risk analysts that joined the team.
    Technologies: MATLAB, Python


  • Realistic Human Face Generator for Mobile App Golden Boot 2019

    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 one 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.

  • Customer Support Automatization with Natural Language Processing

    A natural language processing system to automatically classify customer issues. The tool was developed during my time at Nordeus, a mobile gaming company with over two million daily active users. The end-goal was to reduce the number of tickets that human agents had to process, and increase customer satisfaction overall.

  • Financial Markets Valuation and Explanation Using Machine Learning

    A suite of machine learning models to quantify and explain the valuations of banking institutions in the Gulf region. I was the machine learning engineer in charge of designing and implementing such a system according to the data availability and end goals of the client.

  • Fleet Optimization and Demand Forecasting with Gradient Boosting

    Lola Market is a Spanish startup that lets you order groceries online at your favorite shop and get them delivered to your home. The company has a fleet of shoppers that go to the stores, do the shopping, and take it to the user's house.

    A big question for Lola's operations team was: "how many shoppers should be available at each location and hour of the day to guarantee 100% availability to our users and to minimize shopper idle hours?". The goal of the project was to automate and improve the allocation of shoppers in geographies and timeslot.

    The solution I developed is a machine learning (ML) model that predicts future user demand at each geography (city, district) and hour of the day for the following two weeks. I also developed a suite of Tableau dashboards to make the system transparent to Lola's Operations team.

  • The Hands-on Reinforcement Learning Course

    Created and published an online course on reinforcement learning (RL), from fundamentals to cutting-edge deep RL.

    The course is available online for free.

    The goal is to teach my students, with a hands-on approach, how to implement the key RL algorithms from scratch using Python and PyTorch.

  • 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, C++, Python 3
  • Frameworks

    Flask, Hadoop
  • Libraries/APIs

    Scikit-learn, Keras, TensorFlow, OpenCV, PySpark, REST APIs, Shapely, XGBoost, PyTorch
  • Tools

    Tableau, PyCharm, Impala, MATLAB, Jupyter
  • Paradigms

    Data Science
  • Other

    Machine Learning, Natural Language Processing (NLP), Statistics, Statistical Modeling, Computer Vision, Quantitative Finance, Mathematical Modeling, Data Visualization, Time Series Analysis, Deep Learning, AWS, AWS Database Migration Service, Explainable Artificial Intelligence (XAI), Data Engineering, Segment, Random Forests, Mathematics, Optimization, Genomics, Custom BERT, DeepAR, Deep Reinforcement Learning, Reinforcement Learning, Data Analyst
  • Platforms

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