Gabriel Caceres
Customer Engineer AI ML at Google
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolGabriel Caceres is a Customer Engineer in AI/ML with a decade of experience applying statistical rigor and machine learning to production problems across industry and research. He holds a PhD in Astrophysics and translated large-scale time-series expertise—processing terabytes of Kepler data and discovering candidate exoplanets—into commercial forecasting and segmentation solutions. At companies from SparkBeyond to Teachers Pay Teachers and EY he has led client-facing teams, built end-to-end ML pipelines, and shipped Generative AI applications that include summarization, document question-answering, and agent integrations with external APIs. Gabriel couples hands-on Python/R development and deployment experience with optimization and dynamic-programming know-how, enabling practical, data-driven decision strategies. He also contributed notable enhancements to the widely used forecast R package (improving nnetar behavior and forecast robustness), reflecting a mix of open-source impact and domain depth. Based in New York, he is a practiced communicator who enjoys visualizing complex concepts for both technical and executive audiences.
10 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (PhD) Astrophysics, Doctor of Philosophy (PhD) Astrophysics at Penn State UniversityBachelor of Arts (B.A.) Physics Mathematics Philosophy, Bachelor of Arts (B.A.) Physics Mathematics Philosophy at Augustana College
English, Spanish
Github Skills (9)
neural-network10
forecasting10
model-building10
forecast10
time-series10
statistical-models10
r10
data-analysis10
machine-learning8
Programming languages (3)
Github contributions (5)
robjhyndman/forecast
Jan 2016 - May 2018
Forecasting Functions for Time Series and Linear Models
Role in this project:
Data Scientist
Data ScientistContributions:96 commits, 20 PRs, 48 comments in 2 years 4 months
Contributions summary:Gabriel significantly improved the `forecast` package by adding features to the `nnetar` function and its associated methods. The contributions include incorporating external regressors into the model, fixing typos, adjusting the network size based on the inclusion of regressors, and allowing for the use of an existing model. Further enhancements involved making the `forecast` method more robust by including xreg and bootstrap options and providing prediction intervals.
forecastingr-packagetidymodelscranrstats
gabrielcaceres/dotfiles
Jun 2023 - Oct 2024
Contributions:14 pushes in 1 year 4 months
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