Jo-fai Chow - Council Member at Numerai

Jo-fai Chow

Council Member at Numerai

Greater Reading Area United Kingdom

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Summary

🤩

Rockstar

🎓

Top School

Jo-fai Chow is a data scientist and community-minded technology professional with 12 years of experience transitioning from civil engineering to applied machine learning and developer advocacy. He has driven customer success, evangelism, and community growth at H2O.ai and Datapane, combined with hands-on data science work at Virgin Media and contributions to open-source projects like H2O meetups (including building baseline Random Forest and XGBoost models). Elected to multisig council roles at Numerai and YIEDL, he helps steward funds and initiatives that support decentralized data and AI communities. Based in the Greater Reading Area, he blends technical depth in model building and data engineering with public-facing roles as speaker, educator, and organizer. A practical problem-solver, Jo-fai often moves between environments and tools to make models and data products reproducible and deployable for non‑technical stakeholders.

12 years of coding experience

13 years of employment as a software developer

MSc Environmental Management of Urban Land and Water, MSc Environmental Management of Urban Land and Water at The University of Sheffield

EngD Engineering, EngD Engineering at University of Exeter

BEng Civil Engineering, BEng Civil Engineering at University of Bristol

English, cantonese (chinese), Chinese

Stackoverflow

Stats

1reputation

0reached

1answer

0questions

Github Skills (8)

data-preprocessing10

xgboost10

dataprep10

random-forest10

r10

machine-learning9

gbm6

h2o6

Programming languages (4)

RHTMLJupyter NotebookPython

Github contributions (5)

h2oai/h2o-meetups

Feb 2016 - Jun 2021

Presentations from H2O meetups & conferences by the H2O.ai team

Role in this project:

Back-end Developer

Contributions:220 commits, 1 PR, 182 pushes in 5 years 5 months

Contributions summary:Jo-fai contributed to data preparation steps for H2O meetups and conferences by the H2O.ai team, specifically focusing on data preparation and model building. They added session and system information, implemented data splitting, and built baseline models using Random Forest and XGBoost. The user switched between environments, as reflected in the system information updates.

mxneth2odeep-learningmachine-learningai

councilofelders/meetups

Jul 2022 - Oct 2022

Contributions:25 commits, 63 pushes, 1 branch in 2 months

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