Joost Van Der Linden
Principal Data Scientist
Minneapolis, Minnesota, United States
Summary
👤
Senior🎓
Top SchoolJoost Van Der Linden is a Principal Data Scientist with a decade of experience applying rigorous applied mathematics and scientific computing to deliver measurable business impact via causal analytics and production AI/ML. With a PhD in Infrastructure Engineering and masters in Applied Mathematics, he combines deep numerical algorithm expertise (C++, Python, R) with cloud deployment experience (GCP, AWS) to ship user-facing AI features and optimize scientific models. At Dexcom he progressed through senior technical roles driving experiments, metrics, and model-driven product improvements while mentoring peers. An active contributor to scientific open-source—e.g., enhancing OpenPNM to interoperate with NetworkX—he blends research-grade code quality with pragmatic product focus. Based in Minneapolis, he thrives in roles that let him build end-to-end solutions, from experimental design and inference to scalable deployment.
10 years of coding experience
12 years of employment as a software developer
Minor (1 semester) Business & Management, Minor (1 semester) Business & Management at The University of QueenslandThe University of MelbourneMaster of Science & Honors Program Applied Mathematics, Master of Science & Honors Program Applied Mathematics at Delft University of Technology
Github Skills (5)
networkx10
python10
scientific-computing10
network-graph8
unit-testing8
Programming languages (2)
Github contributions (5)
PMEAL/OpenPNM
Sep 2017 - Sep 2017
A Python package for performing pore network modeling of porous media
Role in this project:
Back-end Developer
Back-end DeveloperContributions:5 commits, 1 PR, 7 comments in 13 days
Contributions summary:Joost focused on enhancing the functionality of the `openpnm` library by integrating with the NetworkX library, a common tool for network structures. The user refactored code to load NetworkX graphs, updated documentation, and added more rigorous checks to ensure data integrity during the loading process. This involved modifying the I/O functionality of the library to support NetworkX graph objects and updating unit tests to confirm correct functionality.
pythonscientific-computingporeporous-materialsporous-media
joosthvanderlinden/joosthvanderlinden.github.io
Jul 2015 - May 2021
Contributions:336 commits, 331 pushes in 5 years 11 months
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