Spencer Romo
Senior Data Scientist
Austin, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolSpencer Romo is a Senior Data Scientist based in Austin with a decade of experience designing and productionizing machine learning systems across startups and enterprise cloud at AWS. He specializes in deep learning, reinforcement learning, and data infrastructure—having contributed backend improvements and reward-function refinements to the prominent FinRL financial reinforcement learning project. Spencer pairs hands-on engineering (from full-stack and cloud-deployed SaaS to geospatial computer vision) with teaching and curriculum work, mentoring practitioners as a data science instructor. His trajectory spans founding a music-tech startup to leading ML efforts at KUNGFU.AI and Slingshot Aerospace, reflecting both product-minded pragmatism and research curiosity. A Texan who calls himself a nerd and teacher, he brings a rare blend of applied ML rigor, systems engineering, and classroom clarity to complex, production ML problems.
10 years of coding experience
8 years of employment as a software developer
Bachelors of Science Business Administration; Economics and Strategy; Computer Science, Bachelors of Science Business Administration; Economics and Strategy; Computer Science at Washington University in St. Louis - Olin Business SchoolUniversity of Texas at San AntonioTMI Episcopal
Github Skills (13)
gymnasium10
openai-gym10
finance10
deep-reinforcement-learning10
pytorch10
tensorflow10
python10
algorithmic-trading10
data-analysis9
caching9
logging8
pandas8
numpy8
Programming languages (3)
Github contributions (5)
AI4Finance-Foundation/FinRL
Jan 2021 - Mar 2021
FinRL: Financial Reinforcement Learning. 🔥
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:32 reviews, 49 commits, 25 PRs in 1 month
Contributions summary:Spencer contributed significantly to the `finrl` repository, primarily focusing on enhancing the stock trading environment. Their work included adding logging capabilities for improved monitoring and debugging. They also implemented data caching mechanisms to optimize performance and improve the environment's efficiency. Furthermore, they updated and refined the stock trading environment to better accommodate the needs of the project by refining the reward functions.
stable-baselinesdrl-frameworkdeep-reinforcement-learningsecfinance
spencerR1992/banded_reboot
Aug 2016 - Oct 2016
Contributions:24 PRs, 74 pushes, 23 branches in 1 month
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