Tennessee Leeuwenburg
Data Science And Emerging Technologies Team Leader
Melbourne, Victoria, Australia
Summary
👤
Senior🎓
Top SchoolTennessee Leeuwenburg is a data science and emerging technologies leader with 11+ years at the Bureau of Meteorology delivering machine learning, scientific computing and HPC solutions for weather and environmental prediction. He combines hands-on technical work—maintaining open source packages like scores and pyearthtools and improving test coverage for foundational projects such as Keras—with strategic leadership in change management, budgeting and capability uplift. His teams have operationalised natural language generation at national scale and migrated critical weather models onto next-generation supercomputers, demonstrating rare domain expertise across research-to-operations transitions. Based in Melbourne, he blends an MBA-backed strategic perspective with deep technical craft, notably applying ML to verification and site-based forecasting where incremental accuracy gains have outsized public-safety impact.
11 years of coding experience
21 years of employment as a software developer
The University of MelbourneMaster of Business Administration (Executive), Master of Business Administration (Executive) at RMIT University
Stackoverflow
1,713reputation
269kreached
2answers
4questions
python
top-5%
database
top-5%
csv
top-1%
sqlite
top-1%
Github Skills (21)
unit-testing10
pytorch10
sqlite10
python10
data-science10
testing10
machine-learning10
keras10
deep-learning10
tensorflow10
neural-network10
csv10
test-automation10
database9
pyglet6
Programming languages (5)
Github contributions (5)
keras-team/keras
Jun 2015 - Aug 2017
Deep Learning for humans
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:17 commits, 15 PRs, 41 comments in 2 years 2 months
Contributions summary:Tennessee primarily focused on improving the test coverage and quality of the Keras library. Their contributions include adding tests for new activation functions like softmax and linear, as well as testing the serialization and deserialization process for metrics and layers. The user also expanded testing to cover batch normalization, ensuring the stability and functionality of the library's core components. These efforts directly enhanced the reliability and robustness of the Keras framework.
pythondata-sciencedeep-learningneural-networksmachine-learning
tleeuwenburg/wordgraph
Aug 2014 - Dec 2019
Thing that makes e
Contributions:81 commits, 2 pushes in 5 years 5 months
react
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