Quantco/glum
Jan 2026 – Jan 2026
1 Month active
Languages Used
Python
Technical Skills
PythonPython programmingbenchmarkingdata analysisdata generationmachine learning
DavidEiglspergerQC
PROFILE
Davideiglspergerqc
David Eiglsperger focused on improving data integrity and benchmarking reliability in the Quantco/glum repository, addressing two critical bugs over the course of a month. He resolved a data leakage issue in the housing data generation pipeline by ensuring target columns were dropped before returning datasets, and enhanced code quality by fixing mypy type annotation errors. Additionally, David updated the benchmarking workflow to maintain compatibility with newer h2o versions, streamlining the process for reproducibility and maintainability. His work leveraged Python and data analysis skills, emphasizing robust code structure and reliable benchmarking, and demonstrated a methodical approach to maintaining high engineering standards.
Overall Statistics
Feature vs Bugs
0%Features
Repository Contributions
2Total
Bugs
2
Commits
2
Features
0
Lines of code
32,329
Activity Months1
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Work History
January 2026
2 Commits
Jan 1, 20262026-01 Quantco/glum monthly summary: Focused on data integrity, correctness, and benchmarking reliability. Delivered critical fixes to eliminate data leakage in housing data generation and updated the benchmarking workflow to be compatible with newer h2o versions, with improvements to code structure and typing.
2 Commits
Jan 1, 20262026-01 Quantco/glum monthly summary: Focused on data integrity, correctness, and benchmarking reliability. Delivered critical fixes to eliminate data leakage in housing data generation and updated the benchmarking workflow to be compatible with newer h2o versions, with improvements to code structure and typing.
January 2026
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
PythonPython programmingbenchmarkingdata analysisdata generationmachine learning
Repositories Contributed To
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