Jonah Lam - Manager Data Science

Jonah Lam

Manager Data Science

Canada

Join Prog.AI to see contacts

Summary

👤

Senior

🎓

Top School

Jonah Lam is a Manager of Data Science with a decade of experience turning actuarial rigor and machine learning into business impact at major Canadian insurers. He progressed from actuarial analyst to senior data scientist and now leads data science teams at Intact, bringing deep expertise in modeling, evaluation, and productionizing analytics. Jonah has hands-on open-source contributions to pyGAM, adding robust scoring and testing for generalized additive models, reflecting a strong emphasis on model validation. Trained in actuarial mathematics and finance, he blends statistical discipline with practical ML engineering to solve complex insurance problems. Based in Canada, he seeks continuous learning and new technical challenges while mentoring teams to deliver measurable outcomes. Notably, his background bridges traditional actuarial methods and modern data science workflows, making him effective at translating models into business decisions.

10 years of coding experience

5 years of employment as a software developer

Diploma of Collegial Studies (DEC), Health Sciences, Diploma of Collegial Studies (DEC), Health Sciences at Champlain College

Bachelor of Science (BSc), Actuarial Mathematics/Finance (Co-op), Bachelor of Science (BSc), Actuarial Mathematics/Finance (Co-op) at Concordia University

English, French

Github Skills (6)

machine-learning10

python10

data-science10

numpy9

scikit8

scikit-learn8

Programming languages (1)

Python

Github contributions (5)

dswah/pyGAM

Dec 2018 - Jun 2019

[HELP REQUESTED] Generalized Additive Models in Python

Role in this project:

Data Scientist

Contributions:8 commits, 1 PR, 2 comments in 6 months

Contributions summary:Jonah contributed to the `pygam` repository by implementing and refining a scoring method (R^2) for the Generalized Additive Models. They added the score method to the parent GAM class and updated the LogisticGAM class to call accuracy. The user also added a test for the score method and refined it. Their work focused on model evaluation and providing insights into the performance of the GAM models within the project.

pythondata-scienceadditive-modelsscientific-computingmachine-learning

JodesL/simple_rl

Mar 2021 - Apr 2021

Contributions:11 PRs, 19 pushes, 9 branches in 1 month

Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.

Request Free Trial