Achim Gädke
Data Scientist
Lower Hutt, Wellington, New Zealand
Summary
👤
Senior🎓
Top SchoolAchim Gädke is a Data Scientist with eight years of industry experience and a PhD in Physics, currently applying statistical modelling and engineering skills at Lynker Analytics in Lower Hutt, New Zealand. He has progressed from research roles in NMR and academia to technical and leadership positions in data science and engineering, including directing the data science team at Montoux. Achim blends rigorous scientific thinking with practical software engineering—contributing full-stack improvements to open-source tools like explainerdashboard to make explainable AI more accessible. His background spans backend systems, OLAP and risk modelling, rapid prototyping, and deployment automation, enabling him to translate complex models into production-ready solutions. Colleagues describe him as someone who connects ideas across research fields to create novel, usable insights rather than purely theoretical results. He brings a curious, hands-on approach to making opaque models interpretable and operational.
8 years of coding experience
16 years of employment as a software developer
Technischen Universität Darmstadt
English, German
Github Skills (12)
data-visualizations10
pandas10
data-visualization10
data-visualisation10
bootstra10
dashboard10
dashjs10
python10
bootstrap10
back-end-development8
front-end-development8
github6
Programming languages (9)
Github contributions (5)
oegedijk/explainerdashboard
Mar 2022 - Jun 2022
Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:5 reviews, 11 commits, 6 PRs in 3 months
Contributions summary:Achim primarily focused on enhancing the `explainerdashboard` library by addressing code deprecation, improving the user interface, and optimizing the codebase. They removed deprecated methods, refactored code to align with updated library versions, and implemented new features, such as dynamically updating dropdown options for better usability. Additionally, they configured base routes and static paths for deployment. They also added and fixed features related to the dashboard.
explainerxai-librarypythonxaishap
achimgaedke/explainerdashboard
Feb 2022 - Nov 2022
Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
Contributions:47 pushes, 23 branches in 8 months
pythondata-sciencedashboardsmachine-learningblackbox
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