David Schneider
Machine Learning Engineer Team Lead at Iguazio (Acquired by McKinsey)
Tel-Aviv District, Israel
Summary
🤩
Rockstar🎓
Top SchoolDavid Schneider is a Machine Learning Engineer Team Lead based in Tel Aviv with five years of engineering experience building production ML systems and feature engineering tooling. He brings strong algorithms and systems skills across Python, C++, C, Matlab, and Java, and has progressed from software and algorithm development to leading ML teams at Iguazio (now part of McKinsey). David has hands-on MLOps experience contributing to MLRun’s Feature Store—fixing OneHotEncoder edge cases and improving offline feature filtering and tests—demonstrating a focus on robust data transformations for production pipelines. His background as an intelligence officer and dual bachelor’s studies alongside an ongoing MSc in Computer Science give him a practical, security-minded approach to complex data problems. Colleagues know him for turning subtle data issues into reliable, test-covered solutions that scale.
5 years of coding experience
5 years of employment as a software developer
Bachelor's degree, Computer Engineering, Bachelor's degree, Computer Engineering at The Hebrew University of JerusalemBachelor's degree, Computer Science, Bachelor's degree, Computer Science at Københavns Universitet - University of CopenhagenMaster of Science - MS, Computer Science, Master of Science - MS, Computer Science at Reichman University
Hebrew, English, Russian
Github Skills (12)
pandas10
machine-learning10
mlops10
feature-engineering10
python10
data-science9
data-engineering9
regex9
regular-expression9
workflow-engine8
kubernetes-pods7
kubernetes7
Programming languages (3)
Github contributions (5)
mlrun/mlrun
Jul 2022 - Jan 2023
MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.
Role in this project:
ML Engineer
ML EngineerContributions:588 reviews, 18 commits, 419 PRs in 6 months
Contributions summary:David primarily contributed to the Feature Store component of the MLRun MLOps platform. Their commits focused on fixing bugs related to the `OneHotEncoder` step, particularly when handling spaces, hyphens, and integer values within categorical features. They also added functionality for filtering offline features and expanded testing for various steps, demonstrating a focus on improving data transformation and feature engineering capabilities.
cicdmachine-learningmlopsmlrunpython
davesh0812/storey
Jun 2022 - Jun 2026
real-time data + ML pipeline
Contributions:226 pushes, 21 branches in 4 years 1 month
pipelinereal-time-datadata-sciencedeep-learningmlops
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