William Teo
Lead Data Scientist
Singapore
Summary
👤
Senior🎓
Top SchoolWilliam Teo is a Lead Data Scientist in Singapore with 11 years of experience building end-to-end ML and cloud-native platforms, from UX and frontend to MLOps, Kubernetes and Terraform-driven infrastructure. He has led data science teams in government and industry—modernizing Grab’s data science platform for 100+ practitioners and driving AI/ML adoption at GIC for investment insights using NLP on alternative data. A pragmatic engineer and human-centered designer, he mentors teams in engineering excellence, GitOps/IaC practices and production-grade ML workflows, and has hands-on open-source contributions to Kubeflow Pipelines spanning backend and frontend improvements. His background in human factors and perception science gives him a rare strength in designing decision-focused analytics that are both usable and operationally robust.
11 years of coding experience
14 years of employment as a software developer
Basic Certificate, Sculpture, Basic Certificate, Sculpture at Nanyang Academy of Fine ArtsB. Eng, Mechanical Engineering, Bioengineering, 2nd Upper Hons., B. Eng, Mechanical Engineering, Bioengineering, 2nd Upper Hons. at National University of Singapore
English, Chinese
Github Skills (16)
kubeflow10
kubernetes10
mlops10
kubeflow-pipelines10
python10
kubernetes-pods10
pipeline10
react9
backend9
front-end-development9
back-end-development9
machine-learning9
docker8
javascript8
data-science8
Programming languages (14)
Github contributions (5)
kubeflow/pipelines
Mar 2019 - Sep 2020
Machine Learning Pipelines for Kubeflow
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:6 reviews, 20 commits, 23 PRs in 1 year 5 months
Contributions summary:William contributed extensively to the Kubeflow Pipelines project, focusing on both backend and frontend aspects. Their contributions include implementing features such as sidecars for `ContainerOp` and enabling environment variables for the Minio client in the API server, as well as adding support for customized artifact locations. They also made significant improvements to the frontend, including rendering artifact previews and adding unit tests. Furthermore, the user worked on pipeline configuration and execution, adding features like setting parallelism limits and TTL after workflow completion.
pipelinetektondata-sciencemachine-learningmlops
eterna2/MRCluster
Nov 2014 - Dec 2014
Contributions:43 commits in 1 month
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