Dayvid Oliveira
Senior Machine Learning Engineer at Apple
Austin, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolDayvid Oliveira is a Senior Machine Learning Engineer with 15 years of experience, currently advancing privacy-preserving ML research and engineering at Apple in Austin. He combines deep academic rigor—a PhD in Machine Learning—with extensive applied experience across hardware and services teams, leading data science and ML initiatives since 2017. His work spans production-grade ML systems, applied privacy techniques, and research-to-product delivery, informed by prior roles in signal, image and text projects. An active open-source contributor, he implemented novel under-sampling methods (including Instance Hardness Threshold) and enhancements to ENN/RENN in the widely used imbalanced-learn library. He has a track record of publishing research, building ensemble-learning tooling, and shipping performant algorithms in C, Java and Python. Pragmatic and research-savvy, he bridges theoretical methods and real-world constraints to deploy robust ML solutions at scale.
15 years of coding experience
11 years of employment as a software developer
Master of Science (MSc), Computer Science, Master of Science (MSc), Computer Science at Federal University of PernambucoBachelor of Engineering (BE), Computer Engineering, Bachelor of Engineering (BE), Computer Engineering at Universidade Federal de Pernambuco
English, Portuguese
Stackoverflow
1,187reputation
212kreached
7answers
25questions
Github Skills (16)
scikit-learn10
scikit10
statistics10
machine-learning10
python10
data-science10
data-analysis10
algorithms9
data-structure9
algorithm9
data-structures9
hashtable6
sizeof6
regex6
rpy26
Programming languages (4)
Github contributions (5)
scikit-learn-contrib/imbalanced-learn
May 2016 - Dec 2016
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
Role in this project:
Data Scientist
Data ScientistContributions:42 commits, 13 PRs, 2 pushes in 6 months
Contributions summary:Dayvid primarily contributed to the implementation and refinement of under-sampling techniques for imbalanced datasets. Their work involved creating a new method, Instance Hardness Threshold, and also expanding functionality for Edited Nearest Neighbours (ENN) and Repeated Edited Nearest Neighbours (RENN). These contributions included coding the algorithms, integrating them into the library, and providing examples.
data-analysispythonstatisticstackledata-science
dvro/scikit-protopy
Jun 2014 - Aug 2015
Contributions:62 commits, 1 PR, 1 push in 1 year 2 months
pythondata-sciencetoolboxmachine-learningscikit-learn
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