Luis Chinchilla-Garcia, and I am:

a Data Engineer

Luis G. Chinchilla-Garcia

Data EngineerMachine Learning EngineerData Science

I am a Data Scientist and Machine Learning Engineer who also delves into the worlds of Data Engineering and Web Development. Currently, I am a Lead Data Scientist at NBC Universal, where I focus on creating scalable end-to-end machine learning models for Recommender Systems.

I graduated from the University of California, Los Angeles with a BS in Astrophysics. During that time, I completed four years of research as part of a research fellowship and was also a co author in a paper (details below).

Skills and Experience

For Machine Learning Engineering, some of my responsibilities have included:

  • Actively researching and prototyping machine learning models to personalize product recommendations utilizing frameworks like Tensorflow and PyTorch
  • Deploying machine learning models on cloud services as part of an end-to-end machine learning workflow
  • Developing & maintaining end-to-end machine learning pipelines that are scalable and cost effective
  • Utilizing frameworks like Tensorflow Extended with AI Platform to develop, test, and deploy machine learning models
Tensorflow Probability
Recommender Systems


Below are some of the most recent projects I have been working on

Probabilistic PCA

  • Extending Principal Component Analysis(PCA) to use a probabilistic approach.
  • In doing so, it is possible to push the use of PCA and generate new outputs.
Tensorflow Probability

Extending Probabilistic Linear Regression

  • Linear regression is a foundational part of mathematics that provides the foundation to machine learning.
  • This project revolves around extending linear regression with probability and its practical uses.
Linear Regression

Music Chord Progressions

  • Utilizing both Sequence and Attention based models to generate a sequence of chords.
  • Data from this project is personally taken using MIDI files or transcriptions of chord progressions in musical pieces.
  • This section is focused on crating the model itself using Tensorflow.



Here are a few notable professional events that include Astrophysics research, and experience in Machine Learning/Data Engineering.

Contact Me


Study of Filaments Near the Galactic Center

Conducted image processing and statistical analysis on data take by space telescopes WISE and SPITZER on gas filaments AFGL-5376 and Double Helix Nebula in the Galactic Center.

(Summer 2016)

Radio Transient Classification

Used clustering techniques to classify fast radio transients. This work was done under the mentorship of Dr. Casey Law and Dr. Carl Heiles.


Search for Technosignature in TRAPPIST-1

Collected over three terabytes of observational data from the 100m Green Bank telescope to identify possible technosignatures near TRAPPIST-1.


Lead Machine Learning Engineer

Leading the data science team toward researching, developing & experimenting machine learning models to solve problems in Natural Language Processing, personalized UI/UX, and business-oriented analysis.


Red Bull

Research, prototype, and deploy machine learning models on cloud services to personalize product recommendations as part of an end-to-end machine learning workflow. Furthermore, created a webapp internal tool to be able to observe recommendations from deployed models with their respective metadata.

Contact Me

I'm always open for discussions, so feel free to contact me!