Randy Gelhausen
Technical Director, Solution Engineering at Hortonworks
Atlanta, Georgia, United States
Summary
🤩
Rockstar🎓
Top SchoolRandy Gelhausen is a Technical Director in Solution Engineering with 12 years of experience turning messy, heterogeneous data into measurable business outcomes across industries from bioinformatics to telco and manufacturing. Trained as a computer engineer, he blends hands-on systems design and coding (Python, Spark, Hadoop ecosystem) with stakeholder-driven metric definition to help organizations unlock new revenue and efficiency. At Hortonworks he architects data platforms and writes production code, while earlier roles at Accenture and Sealed Air honed his full-stack and plant analytics chops. He’s an active open-source contributor in the RAPIDS ecosystem—adding practical cuDF/cuML examples and end-to-end notebooks that demonstrate GPU-accelerated data science workflows. Colleagues rely on him not just for technical direction but for inventing novel analyses and pragmatic ways to operationalize measurement.
11 years of coding experience
2 years of employment as a software developer
B.S. Computer Engineering, B.S. Computer Engineering at Clemson University
English, German
Stackoverflow
125reputation
5kreached
0answers
4questions
Github Skills (22)
algorithm10
xgboost10
python10
dataframes10
cudf10
machine-learning10
machine-learning-algorithms10
dataframe10
data-transformation10
rapids10
dask10
gpu10
cuda10
jupyter-notebook10
cuml10
Programming languages (11)
Github contributions (5)
rapidsai-community/notebooks-contrib
Feb 2019 - Sep 2022
RAPIDS Community Notebooks
Role in this project:
Data Scientist
Data ScientistContributions:2 reviews, 96 commits, 14 PRs in 3 years 7 months
Contributions summary:Randy's contributions primarily revolve around creating and enhancing a notebook focused on predicting NYC taxi fares. This involved loading, inspecting, and transforming data using cuDF, Dask, and other RAPIDS libraries. The user implemented machine learning model training with XGBoost and evaluated the model's performance, demonstrating a focus on end-to-end data science tasks. Model persistence using dill and Google Cloud Storage was also implemented.
jupyter-notebooknotebooksrapids
rapidsai/cudf
Apr 2018 - Mar 2019
cuDF - GPU DataFrame Library
Role in this project:Data Scientist
Data ScientistContributions:4 reviews, 73 commits, 29 PRs in 11 months
Contributions summary:Randy primarily contributed to adding examples and improving the documentation for the `cudf` library. Their work involved demonstrating the use of the `one_hot_encoding` function and providing practical examples for various DataFrame methods. They also focused on creating examples and documentation related to the `read_csv` function.
cudadataframe-librarydata-analysiscppcudf
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