Jeremy Goh
Low Code Business Analyst at Temus
Singapore
Summary
🤩
Rockstar🎓
Top SchoolJeremy Goh is a Low Code Business Analyst and former AI & Data consultant with eight years of experience blending data science rigor and practical business thinking. Based in Singapore, he has moved from Deloitte analytics projects into low-code solutions at Temus, consistently prioritizing reliability, clear communication, and calm execution under pressure. A hands-on contributor to notable open-source ML and data tooling—such as improvements to SHAP, PyJanitor, Polars, and StellarGraph—he focuses on code quality, CI robustness, documentation, and reproducible examples. His background in business and accounting (1st Class Hons, University of Exeter) gives him a rare cross-functional fluency that helps translate analytical models into usable business processes. Known for adaptability and relationship-building, he combines meticulous backend/DevOps fixes with an eye for user-facing clarity.
8 years of coding experience
BSc (Hons) Business and Accounting, Finance, 1st Class Honours, BSc (Hons) Business and Accounting, Finance, 1st Class Honours at University of Exeter
Github Skills (31)
interpretation10
shap10
python10
data-science10
scikit10
dataframes10
pandas10
machine-learning10
polar10
dataframe10
numpy10
deeplearning-ai10
explainable-artificial-intelligence10
deep-learning10
data-cleaning10
Programming languages (13)
Github contributions (5)
shap/shap
May 2023 - Sep 2024
A game theoretic approach to explain the output of any machine learning model.
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:261 reviews, 166 PRs, 239 pushes in 1 year 4 months
Contributions summary:Jeremy's commits primarily involved code refactoring and improvements to the project's infrastructure and build process. This includes the deprecation and removal of legacy dependencies (distutils), adjustments to the setup.py file, and fixing deprecation warnings. Furthermore, the user implemented enhancements to support continuous integration and testing through the addition of test cases. The user also addressed several documentation typos and addressed issues with the project's build configuration by integrating TensorFlow and making adjustments to support other dependency setups.
explaininterpretabilityshapdeep-learningapproach
pyjanitor-devs/pyjanitor
Jan 2022 - Jan 2023
Clean APIs for data cleaning. Python implementation of R package Janitor
Role in this project:Backend Developer
Backend DeveloperContributions:163 reviews, 37 commits, 51 PRs in 1 year
Contributions summary:Jeremy's commits primarily focused on improving the documentation and examples within the `pyjanitor` library, specifically updating docstrings to include minimal working examples (MWEs). They also contributed to deprecating parameters and allowing keyword arguments in the `bin_numeric` function, as well as fixing an issue related to the `filter_on` complement option. The contributions included fixes related to Black formatting and minor refactoring to existing functions. This suggests a focus on code quality and usability of the library.
cleaningr-packagepythondatadata-science
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