Publication
Adversarial Machine Learning: How to Attack and Defend ML Models
http://l2d.litpliant.net/machine-learning/adversarial-machine-learning-tutorial
Pau is available for hire
Hire PauPau Labarta Bajo
Verified Expert in Engineering
Mathematical Modeling Developer
Location
Barcelona, Spain
Toptal 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.
Portfolio
EasyHealth
Google Cloud Platform (GCP), Python
Cogsy Limited
Amazon Web Services (AWS), Python 3, DeepAR
Speakeasy Labs
REST APIs, Segment
Experience
Availability
Part-time
Preferred Environment
Amazon Web Services (AWS), PyCharm, MacOS
The most amazing...
...我建立的模型是一个生成式神经网络，它可以为手机游戏中的足球运动员创建逼真的头像.
Work Experience
Machine Learning Engineer
2021 - PRESENT
EasyHealth
- Developed ML-based bidding bot to acquire leads more cost-effectively.
- 开发客户流失预测模型，预测政策流失，提高客户保留率.
- 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.
- 帮助定义和执行一个事件跟踪系统，以适应新的iOS 14跟踪限制.
- Advised the client on specific low-level details related to Segment.io.
Technologies: REST APIs, Segment
Machine Learning Engineer
2020 - 2020
Lola Market - Freelance
- 开发、部署和维护机器学习模型，以提高购物者的效率.
- Bootstrapped the company's first data warehouse and reporting layer, including Amazon Redshift, Amazon Database Migration Service (DMS), and Tableau.
- 开发了多个仪表板，帮助客户提高车队管理效率.
Technologies: Amazon Web Services (AWS), Python, Scikit-learn
Machine Learning Engineer | Statistician
2020 - 2020
Toptal Client
- 使用可解释的机器学习分析海湾地区的金融市场估值.
- 编写了一个Python包，以确保分析的每个步骤的内部再现性, including data processing, data validation, data visualization, model construction, model validation, and model explanation.
- 对一系列ML解决方案进行基准测试，并对其进行微调，以提高模型的准确性和可解释性.
Technologies: Shapely, Statistics, Scikit-learn, Machine Learning, Python
Explainable AI Engineer
2020 - 2020
15kay
- Supported the development of a scientific Python package in the medical field.
- 研究包在开源ML和AI生态系统中的适用性.
- Created tutorial notebooks to showcase potential uses of the package.
技术:可解释人工智能(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.
- 开发机器学习模型，提高萝拉市场的运营效率, a client of Goguru. Lola Market为顾客提供在线购买食品杂货的服务，并在几小时内送货上门.
Technologies: Random Forests, Scikit-learn, Amazon Web Services (AWS), Tableau, Python, AWS Database Migration Service (DMS), Redshift
Data Visualization | Data Engineer
2019 - 2020
Cyngn
- 在Cyngn上创建、更新和维护数据分析堆栈的前端仪表板.
- 在Tableau中开发快速可视化原型，并将其部署到工程团队可以访问的仪表板中.
- Developed components of the internal ETL tool in Python and SQL.
- 帮助后端工程师在Amazon Redshift中集成前端和后端堆栈.
Technologies: Amazon Web Services (AWS), SQL, Tableau, Redshift
Mathematical C++ Developer (Genetics Project)
2019 - 2019
Confidential
- 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
- 开发统计和机器学习模型，以了解金融机构的市场估值.
- 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
- 创建了一个神经网络模型，以可扩展的方式生成足球运动员的面孔. The outputs from this model are used in one of the company's games.
- Designed matchmaking algorithms in the Top Eleven game, 一个足球经理模拟使用博弈论和蒙特卡罗技术在全球超过2亿用户.
- 与内部客户支持团队合作，使用NLP技术自动标记玩家投诉的过程.
- 开发一个预测模型来估计营销活动的ROAS(广告支出回报).
- 管理两名初级数据科学家，负责商业智能和游戏系统设计.
Technologies: Scikit-learn, Tableau, Impala, Hadoop, Python, Data Analysis
Quantitative Risk Analyst
2012 - 2016
Erste Group Bank
- 在MATLAB和Python中实现并验证了Erste Group Bank用于定价和对冲利率衍生品的所有模型.
- 为每个经过验证的模型编写详尽的文档，提交给欧洲中央银行.
- 对用于估计银行和交易帐簿的信贷市场风险的方法提出并实施改进.
- 对不同风险价值模型的性能进行回溯测试，以对银行使用的方法提出改进建议.
- Mentored junior quantitative risk analysts that joined the team.
Technologies: MATLAB, Python
Experience
Realistic Human Face Generator for Mobile App Golden Boot 2019
我想要解决的问题是完全自动化为公司的几款游戏生成足球运动员个人资料图像的过程. 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, 一个尖端的GAN网络，经过我自己的数据集的重新训练，生成了逼真的足球运动员面孔. 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, 在VGG网络的最后一层之上进行另一种逻辑回归，根据种族对人脸进行分类. 这最后一步是必要的，以便控制足球运动员的国籍和他的外表之间的相关性.
I built a pipeline of three models, each applied sequentially. First, 一个尖端的GAN网络，经过我自己的数据集的重新训练，生成了逼真的足球运动员面孔. 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, 在VGG网络的最后一层之上进行另一种逻辑回归，根据种族对人脸进行分类. 这最后一步是必要的，以便控制足球运动员的国籍和他的外表之间的相关性.
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
一套机器学习模型，用于量化和解释海湾地区银行机构的估值. 我是机器学习工程师，负责根据客户的数据可用性和最终目标设计和实现这样一个系统.
Fleet Optimization and Demand Forecasting with Gradient Boosting
http://lolamarket.com/es/en/Lola Market是一家西班牙初创公司，它可以让你在你最喜欢的商店网上订购杂货，然后送货上门. The company has a fleet of shoppers that go to the stores, do the shopping, and take it to the user's house.
Lola的运营团队面临的一个大问题是:“在一天中的每个地点和时间，应该有多少购物者可以使用，以保证我们的用户100%可用，并最大限度地减少购物者空闲时间。?". 该项目的目标是自动化和改进地理位置和时间段的购物者分配.
我开发的解决方案是一个机器学习(ML)模型，可以预测每个地理位置(城市)未来的用户需求, district) and hour of the day for the following two weeks. 我还开发了一套Tableau仪表板，使系统对Lola的运营团队透明.
Lola的运营团队面临的一个大问题是:“在一天中的每个地点和时间，应该有多少购物者可以使用，以保证我们的用户100%可用，并最大限度地减少购物者空闲时间。?". 该项目的目标是自动化和改进地理位置和时间段的购物者分配.
我开发的解决方案是一个机器学习(ML)模型，可以预测每个地理位置(城市)未来的用户需求, district) and hour of the day for the following two weeks. 我还开发了一套Tableau仪表板，使系统对Lola的运营团队透明.
The Hands-on Reinforcement Learning Course
http://datamachines.xyz/the-hands-on-reinforcement-learning-course-page/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.
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.
Skills
Languages
Python, SQL, C++, Python 3
Frameworks
Flask, Hadoop
Libraries/APIs
Scikit-learn, Keras, TensorFlow, OpenCV, PySpark, REST api, shaely, 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, GPT, Generative Pre-trained Transformers (GPT), Time Series Analysis, Deep Learning, AWS Database Migration Service (DMS), Explainable Artificial Intelligence (XAI), Data Engineering, Segment, Random Forests, Mathematics, Optimization, Genomics, Custom BERT, DeepAR, Deep Reinforcement Learning, Reinforcement Learning, Data Analysis
Platforms
Google Cloud Platform (GCP), Amazon Web Services (AWS), MacOS
Storage
Redshift
Education
2011 - 2012
Master's Degree in Quantitative Economics
Ca'Foscari University Venice - Venice, Italy
2010 - 2011
Master's Degree in Quantitative Economics
University of Bielefeld - Bielefeld, Germany
2005 - 2010
Master's Degree in Mathematics
Polytechnic University of Catalonia - Barcelona, Spain
Certifications
JULY 2005 - PRESENT
Participant in the 46th International Mathematical Olympiad
International Mathematical Olympiad
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