Julia Schiffner
Software- Und Algorithmenentwicklerin at QASS GmbH
Dusseldorf, North Rhine-Westphalia, Germany
Summary
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Rockstar🎓
Top SchoolJulia Schiffner is a software and algorithms developer with 11 years of experience combining applied statistics and machine learning engineering from academic research to industry practice in Düsseldorf. With a Diplom in Statistics from TU Dortmund and roles at TU Dortmund, the University of Düsseldorf and QASS GmbH, she builds reliable algorithms and prepares complex datasets for real-world ML tasks. An active member of the mlr-org on GitHub, she has contributed numerous curated datasets and documentation improvements to the well-known Machine Learning in R ecosystem, improving usability across classification, regression, survival and clustering workflows. Her background in scientific research gives her a strong foundation in rigorous experimental design and reproducible data preparation, often surfacing corner-case data fixes that quietly raise model quality. Colleagues rely on her for meticulous dataset engineering and clear technical communication bridging research and production.
11 years of coding experience
Diplom, Statistik, Diplom, Statistik at Technische Universität Dortmund
German, English
Github Skills (14)
data-preprocessing10
survival-analysis10
regression10
machine-learning10
dataprep10
predictive-modeling10
clustering10
data-science10
classification10
r10
datasets9
r-package9
statistics8
user-manual7
Programming languages (3)
Github contributions (5)
mlr-org/mlr
Sep 2014 - Oct 2016
Machine Learning in R
Role in this project:
Data Scientist
Data ScientistContributions:71 commits, 26 PRs, 71 pushes in 2 years
Contributions summary:Julia contributed significantly to the dataset creation and preparation within the mlr package. This included adding and preparing several new datasets for various machine learning tasks such as classification, regression, survival analysis, and clustering, including code that used datasets from other packages like mlbench, survival, and cluster. Additionally, the user fixed typos and documentation issues, improving the usability and clarity of the package.
imbalance-correctionlearnersensemble-learningclassificationr-package
mlr-archive/mlr-tutorial
Jun 2015 - Apr 2017
The mlr package online tutorial
Contributions:273 commits, 18 PRs, 151 pushes in 1 year 9 months
mlronline-tutorial
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