Christopher Hench
Principal Scientist at Amazon
Boston, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolChristopher Hench is a Principal Scientist at Amazon with a decade of experience bridging academic research and production ML systems, holding a PhD from UC Berkeley and fellowships at the Berkeley Institute for Data Science and D-Lab. He has progressed through research and applied-science roles at Amazon, shipping language and deployment-focused features in production, and contributes to notable open-source projects such as NLTK (implementing a syllabification algorithm) and JupyterHub Kubernetes tooling. Comfortable across NLP, back-end engineering, and DevOps, he pairs rigorous algorithmic thinking with pragmatic engineering—evident in both library-level linguistic code and Helm chart improvements for large-scale launches. Based in Boston, he combines deep academic grounding with hands-on deployment experience and a quiet breadth of skills including translation and teaching that inform his interdisciplinary approach.
10 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Doctor of Philosophy (Ph.D.) at University of California, BerkeleyBachelor of Arts (B.A.), Bachelor of Arts (B.A.) at Middlebury CollegeMaster of Arts (M.A.), Master of Arts (M.A.) at University of Massachusetts AmherstHigh School Diploma, High School Diploma at Wayzata High School
English, German, Swedish, norse, old, german, middle high (ca.1050-1500), german, old high (ca.750-1050), French, Latin, Greek
Github Skills (16)
nltk10
kubernetes10
algorithms10
jupyterhub10
helm10
devops10
python10
natural-language-processing10
kubernetes-pods10
testing9
yaml9
docker8
gcp8
dockers8
github-oauth7
Programming languages (8)
Github contributions (5)
nltk/nltk
Dec 2018 - Apr 2021
NLTK Source
Role in this project:
Back-end Developer
Back-end DeveloperContributions:11 commits, 3 PRs, 12 comments in 2 years 4 months
Contributions summary:Christopher primarily contributed to the implementation of a new syllabification algorithm within the NLTK library. This involved creating the `SonoritySequencingPrinciple` class, integrating it into the existing tokenization framework, and adding associated tests. The user also refactored code and addressed comments to improve code quality, incorporating the Legality Principle and onset maximization.
nlppythonmachine-learningnltknatural-language-processing
jupyterhub/zero-to-jupyterhub-k8s
Jul 2017 - Aug 2017
Helm Chart & Documentation for deploying JupyterHub on Kubernetes
Role in this project:DevOps Engineer
DevOps EngineerContributions:5 commits, 4 PRs, 5 comments in 21 days
Contributions summary:Christopher primarily focused on improving the deployment and configuration aspects of JupyterHub on Kubernetes. Their contributions included adding timeout parameters to the helm install command for handling large Docker image pulls. They also modified documentation by incorporating a whitelist for GitHub usernames and providing configuration examples for the GitHub OAuth. Furthermore, they fixed a syntax error within a helm install command.
deployingcharthelmjupyter-notebookhelm-chart
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