Matteo Santamaria
Quantitative Researcher
Oakland, California, United States
Summary
👤
Senior🎓
Top SchoolMatteo Santamaria is a quantitative researcher and software engineer with a decade of experience building high-performance systems for finance and data processing. He combines PhD-level modeling from UC Berkeley and an MS from Stanford with hands-on engineering—most recently writing a C++ parser that decoded 60TB of Tokyo Stock Exchange tick data for Verition. His background spans low-latency market-making at WH Trading, mortgage ETL pipelines, and an LLM-based safety triage service at Amazon AIR, reflecting fluency across C++, Python, Rust bindings, and production ML. An active open-source contributor, he has improved the Python bindings and tests for the multithreaded Polars dataframe project. Known for meticulous automation and organization in fast-paced environments, he thrives on turning messy raw data into reproducible, backtestable signals.
11 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD Industrial Engineering & Operations Research, Doctor of Philosophy - PhD Industrial Engineering & Operations Research at University of California, BerkeleyBachelor of Arts - BA Economics and Computer Science, Bachelor of Arts - BA Economics and Computer Science at Amherst CollegeMaster of Science - MS Mathematical & Computational Engineering, Master of Science - MS Mathematical & Computational Engineering at Stanford University
Spanish, English
Github Skills (11)
dataframes10
polar10
rust10
polars10
python10
dataframe10
testing10
arrow-js9
documentation9
arrows9
apache-arrow9
Programming languages (12)
Github contributions (5)
pola-rs/polars
Aug 2022 - Nov 2022
Extremely fast Query Engine for DataFrames, written in Rust
Role in this project:
Backend Developer
Backend DeveloperContributions:54 reviews, 28 commits, 40 PRs in 3 months
Contributions summary:Matteo primarily contributed to the Python codebase of the Polars project, focusing on the `py-polars` module. Their work involved enhancing existing functionality, such as adding new features and refining existing ones. The commits also included writing and modifying tests to ensure the quality and correctness of the library, as evidenced by the changes across `testing.py`, `run_doc_examples.py`, and `test_dataframe.py`. Additionally, the user addressed documentation and examples to improve the user experience of the Polars library.
dataframedataframesquery-enginerustdataframe-library
matteosantama/polars
Jul 2022 - Dec 2022
Fast multi-threaded DataFrame library in Rust | Python | Node.js
Contributions:200 pushes, 63 branches in 4 months
pythondataframesrustdataframenode-js
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