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I am a Machine Learning Scientist at PayPal as well as a recent graduate from the Master of Engineering in Industrial Engineering and Operations Research at the University of California, Berkeley.
My main interests lie in the areas of statistical learning and optimization. I love learning about all things data, engineering, ML and decision science. Bay Area resident and always happy to connect!
In this website you will find my latest personal projects, resume and blog.
I wanted to get my feet wet with the OpenAI API and leverage the power of their GPT models, so I built a quick-and-dirty CLI (with the help of ChatGPT) that will help you find the Linux shell commands that you need for your tasks, right from the command line.
Check it out here.
I recently implemented a simple but quite interesting method for subset selection in regression as a scikit-learn compatible package.
This work was inspired by one of my old grad school projects, in which I compared several statistical learning methods that aim to solve, either exactly or approximately, the best subset selection problem in linear regression. Here you can download the full paper and here you may find my original code.
Recently, I have been learning the basics of Bayesian inference and I am creating a series of "explainers" to share what I believe are some of the key concepts in Bayesian stats in a visual way
- A gentle introduction to Bayesian inference - Part 1
- A gentle introduction to Bayesian inference - Part 2: Monte Carlo and MCMC
In this series of notebooks I analyze the hourly electricity load for PG&E, a Californian utility company, and develop models to forecast electricity demand.
- Exploratory Data Analysis
- Long-term Energy Forecasting with Prophet
- Short-term Energy Forecasting with GAMs
Aug. 2019 - May 2020 : MEng, Industrial Engineering and Operations Research; University of California, Berkeley
*Specialization in Data Science, Statistical Modeling, Machine Learning and Optimization*
Sep. 2017 - May 2020 : MS, Industrial Technology Engineering; Technical University of Madrid
Sep. 2013 - Sep. 2017 : BS, Industrial Technology Engineering; Technical University of Madrid
*Graduated in the **top 3%** of the class*
Mar. 2021 - Present : Machine Learning Scientist; PayPal
- Developing end-to-end machine learning solutions, from data collection to model deployment, within the Payments domain
Jul. 2020 - Feb. 2021 : Analytics Associate; 159 Solutions, Inc.
- Coordinated on-shore and off-shore teams to support the operation and development of a custom reporting & CRM platform serving 100+ users
- Worked directly with Project Manager and client to develop sales force performance analyses leveraging an AWS Data Warehouse and SQL
Nov. 2018 - Jul. 2019 : Machine Learning Research Assistant; Technical University of Madrid
Funded by Collaboration Grant from the Technical University of Madrid
- Processed gait signals from medical trials using Python to build time-frequency data representations
- Investigated machine learning models for neurodegenerative disease diagnostics
- Trained and validated deep learning models (1D and 2D Convolutional Neural Networks) with keras
Feb. 2018 - Jul. 2018 : Business Intelligence Intern; Stratebi Business Solutions
- Constructed data warehouse to streamline the analysis of 1M+ records from Supply Chain data using SQL & Online Analytical Processing tools
- Implemented Extraction, Transformation and Loading (ETL) processes to integrate sales and forecast data, reducing processing time by 98%
- Trained 20+ professionals from the Inspection and Certification industry in Microsoft PowerBI
Programming languages : Python, R, SQL, BASH, Matlab
Libraries and frameworks : numpy, matplotlib, pandas, scikit-learn, statsmodels, pytorch, tensorflow, keras, xgboost, lightgbm, H2O, Hadoop, Spark
Research interests : Experimental Design and Analysis, Time Series Analysis, Deep Learning, Computer Vision, Natural Language Processing
Download PDF version here