Python/ML Developer
2020 - PRESENT15kay (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, TensorFlowML engineer
2020 - 2020Lola 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, AWSMachine learning engineer
2020 - 2020Toptal 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-learnData Scientist
2019 - 2020Goguru (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, TableauData Scientist
2019 - 2020Cyngn (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, SQLMathematical C++ developer (genetics project)
2019 - 2019Confidential- Reviewed and documented the proprietary algorithm that performs base calling.
Technologies: OpenCV, C++ML 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.
- Placed a special emphasis 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
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
Portfolio
- 15kay (via Toptal)Python, Jupyter, TensorFlow
- Lola Market - FreelancePython, AWS
- Toptal clientPython, Explainable ML (Shap), Scikit-learn
Experience
- Mathematical Modeling 11 years
- Quantitative Finance 8 years
- Python 7 years
- Natural Language Processing (NLP) 3 years
- TensorFlow 3 years
- Computer Vision 3 years
- Amazon Web Services (AWS) 2 years
- Custom BERT 1 year
Availability
Full-time
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.
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, TensorFlow, OpenCV, PySparkTools
TableauParadigms
Data ScienceOther
Machine Learning, Natural Language Processing (NLP), Statistics, Statistical Modeling, Computer Vision, Quantitative Finance, Pyspark, Mathematical Modeling, Time Series Analysis, Deep Learning, Genomics, Custom BERTPlatforms
Google Cloud Platform (GCP), Amazon Web Services (AWS)Storage
Redshift
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
Certifications
- Participant in the 46th International Mathematical OlympiadJULY 2005 - PRESENTInternational Mathematical Olympiad
