Matt Turk
Senior Machine Learning Researcher at SCGC Search
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolMatt Turk is a Senior Machine Learning Researcher and engineer with 11 years of experience building data-centric AI and quantitative systems across fintech, VC, and startup ecosystems. Based in NYC, he has led ML efforts at Cleanlab—contributing to its widely used open-source data-quality toolkit and authoring tutorials that improve model robustness on noisy real-world data—and now helps define the data layer of AI at Protege’s DataLab. His background blends production ML, quantitative research at Coinbase and Goldman Sachs, and investor-facing ML at Goodwater Capital, giving him a rare perspective on both model-driven products and investment thesis generation. He’s skilled at turning research into production, from hedging models with an awarded patent to real-time PnL and crypto-native quant systems. Beyond engineering, he scouts VC talent and builds tooling that surfaces data reliability issues, reflecting a passion for trustworthy AI and operationalizing research.
11 years of coding experience
7 years of employment as a software developer
Bachelor's Degree, Electrical Engineering and Computer Science, Bachelor's Degree, Electrical Engineering and Computer Science at University of California, BerkeleyBarron Collier High SchoolQuest for Success
English, Spanish
Github Skills (13)
machine-learning10
data-quality10
data-cleaning10
python10
ai10
scikit-learn9
scikit9
pandas9
xgboost9
exploratory-data-analysis8
data-labeling4
data-science4
active-learning4
Programming languages (6)
Github contributions (5)
cleanlab/cleanlab
Jan 2024 - Sep 2024
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
Role in this project:
Data Scientist
Data ScientistContributions:13 reviews, 9 PRs, 42 pushes in 8 months
Contributions summary:Matt primarily contributed to a tutorial demonstrating data curation techniques within the cleanlab/cleanlab repository, focusing on handling noisy training and test data. Their work involved fixing instantiation issues, adding a new tutorial on data curation with train/test splits, and improving the hyperparameter optimization section. These modifications aimed to improve the robustness of machine learning models and the reliability of performance evaluation on potentially noisy datasets.
operationdata-preprocessingtext-annotationreal-world-datadata-labeling
ccurme/yolopandas
May 2023 - Sep 2024
Contributions:3 reviews, 2 PRs, 6 comments in 1 year 5 months
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