Data Scientist and Machine Learning Engineer
2019 - 2019Toptal 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.
- Special emphasis was placed on model interpretability.
Technologies: Python, Jupyter, Scikit-learnData Scientist and Machine Learning Engineer
2016 - 2019Nordeus- Created a neural network that is able to generate football player faces and that is used in one of the company’s 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, TableauQuantitative Risk Analyst
2012 - 2016Erste 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
Quantitative Finance Developer in Belgrade, Serbia
Member since January 23, 2019
Pau has over seven years of experience in quantitative finance and mobile gaming. He combines a deep understanding of statistics and machine learning with excellent Python skills. He enjoys extracting information from big datasets, combining classical statistical methods and machine learning techniques. He has built software for diverse problems like asset pricing, customer lifetime value prediction or automatic art generation for mobile games.
Pau is now available for hire
Portfolio
- Toptal clientPython, Jupyter, Scikit-learn
- NordeusPython, Hadoop, Impala, Tableau
- Erste Group BankPython, Matlab, Calypso
Experience
- Quantitative Finance, 7 years
- Python, 5 years
- Machine Learning, 5 years
- Deep Learning, 3 years
- Natural Language Processing (NLP), 3 years
- Computer Vision, 2 years
- PySpark, 1 year
- Amazon Web Services (AWS), 1 year
Availability
Part-time
Preferred Environment
Mac, PyCharm, Jupyter, Hadoop, Impala
The most amazing...
...model I've built is a generative neural network that creates realistic profile pictures for football players in a mobile game.
Employment
Experience
- Realistic Human Face Generator for Mobile App Golden Boot 2019 (Development)https://play.google.com/store/apps/details?id=com.nordeus.goldenboot&hl=en
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.
Skills
Languages
Python, SQLFrameworks
FlaskLibraries/APIs
Scikit-learn, Keras, PySpark, TensorFlow, LSTM, OpenCVTools
TableauParadigms
Data ScienceOther
Machine Learning, Natural Language Processing (NLP), Statistics, Statistical Modeling, Computer Vision, Quantitative Finance, Pyspark, Time Series Analysis, Deep Learning, Recommendation Systems, GenomicsPlatforms
Google Cloud Platform (GCP), Amazon Web Services (AWS)
Education
- Master's degree in Quantitative Economics2011 - 2012Ca'Foscari University Venice - Venice, Italy
- Master's degree in Quantitative Economics2010 - 2011University of Bielefeld - Bielefeld, Germany
- Master's degree in Mathematics2005 - 2010Polytechnic University of Catalonia - Barcelona, Spain
