Floid Gilbert
Principal Data Scientist
Twin Falls, Idaho, United States
Summary
👤
Senior🎓
Top SchoolFloid Gilbert is a Principal Data Scientist with nine years of experience blending formal statistical training and professional software development to deliver practical AI/ML solutions. Based in Twin Falls, Idaho, he combines mastery of frequentist and Bayesian methods with hands-on engineering—authoring R/Java integration packages and a multi-threaded MCMC sampler during graduate work. He has contributed to high-profile open-source explainability projects like AIX360 and SHAP, improving feature engineering transformers and decision-plot visualizations to make models more interpretable and robust. Prior roles include independent AI/ML consulting and a decade-plus software engineering career where he led a successful platform migration and built mission-critical utilities for municipal systems. Known for shipping production-ready tooling and bridging research with reproducible software, he brings a pragmatic, stability-focused approach to trustworthy AI.
9 years of coding experience
15 years of employment as a software developer
Master's degree, Statistical Science, Master's degree, Statistical Science at Brigham Young University
Github Skills (16)
scikit10
data-visualizations10
pandas10
machine-learning10
data-visualization10
data-visualisation10
artificial-intelligence10
interpretation10
explainable-artificial-intelligence10
feature-engineering10
shap10
python10
scikit-learn10
numpy9
deep-learning8
Programming languages (4)
Github contributions (5)
Trusted-AI/AIX360
Mar 2020 - Jan 2022
Interpretability and explainability of data and machine learning models
Role in this project:
ML Engineer
ML EngineerContributions:6 commits, 3 PRs, 6 comments in 1 year 10 months
Contributions summary:Floid primarily contributed to the `FeatureBinarizerFromTrees` class, a core component of the AIX360 library focused on explainable AI. Their work included implementing and refining the transformer, including updates for compatibility with newer versions of NumPy, Scikit-learn, and Pandas. They also fixed bugs, addressed unit test failures, and made minor improvements to example notebooks. This indicates a focus on improving the functionality, stability, and usability of feature engineering tools for model interpretability.
explainable-mlibm-research-aitrusted-aicodaitdeep-learning
shap/shap
Jul 2019 - Apr 2020
A game theoretic approach to explain the output of any machine learning model.
Role in this project:Data Scientist
Data ScientistContributions:21 commits, 6 PRs, 9 comments in 9 months
Contributions summary:Floid contributed to the development of decision plots within the shap library. Their work involved implementing examples and updating documentation, including explanations of basic features. Their contributions showcase skills in visualizing machine learning model behavior and explaining predictions. Furthermore, the user adjusted code to enhance the plots and address errors.
explaininterpretabilityshapdeep-learningapproach
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