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

Employment

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 to define and implemented an event tracking system adapted to the new iOS 14 tracking restrictions.
- Advised the client on specific low-level details related to Segment.io.
Technologies: REST APIs, Segment
Machine Learning Engineer
2020 - 2020
Lola Market - Freelance
- Developed, deployed, and maintained a ML model to improve the efficiency of the shoppers' fleet.
- Bootstrapped the first data-warehouse and reporting layer in the company (Amazon Redshift, Amazon DMS, and Tableau).
- Developed several dashboards to help the client improve its fleet management efficiency.
Technologies: Amazon Web Services (AWS), AWS, Python
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: data processing, data validation, data visualization, model construction, model validation, and model explanation.
- Benchmarked a range of ML solutions and fine-tunned 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
Cyngn
- 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
Confidential
- Reviewed and documented the proprietary algorithm that performs base calling.
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
Nordeus
- 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 Top11 game (a soccer manager simulation with over 200M 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 ROAS (return on ad spend) of the marketing campaigns.
- Managed two junior data scientists responsible for business intelligence and game system design.
Technologies: Scikit-learn, Tableau, Impala, Hadoop, Python
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: MATLAB, Python

Experience

Realistic Human Face Generator for Mobile App Golden Boot 2019
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 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
https://lolamarket.com/es/en/
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.
Adversarial Machine Learning: How to Attack and Defend ML Models (Publication)
https://www.toptal.com/machine-learning/adversarial-machine-learning-tutorial
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, SQL, C++, Python 3
Frameworks
Flask, Hadoop
Libraries/APIs
Scikit-learn, Keras, TensorFlow, OpenCV, PySpark, REST APIs, Shapely, XGBoost
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, 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
Platforms
Google Cloud Platform (GCP), Amazon Web Services (AWS), MacOS
Storage
Redshift

Mathematical Modeling Developer in Barcelona, Spain

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Employment

Time Series ML Engineer

Data Engineer

Machine Learning Engineer

Machine Learning Engineer | Statistician

Explainable AI Engineer

Data Scientist | Data Engineer

Data Visualization | Data Engineer

Mathematical C++ Developer (Genetics Project)

Machine Learning Engineer

Data Scientist | Machine Learning Engineer

Quantitative Risk Analyst

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