Paul Lam
Advisor, Applied A.I. at Motiva AI
Tokyo, Japan
Summary
🤩
Rockstar🎓
Top SchoolPaul Lam is an applied AI advisor and founding engineer with 15 years of experience building profitable B2B products from zero, most recently scaling Motiva AI’s ML platform to deliver 60% campaign lifts for Fortune 100 clients while running a highly efficient team. He’s launched five ventures—two profitable, one self-sustaining, and two instructive failures—which taught him rigorous customer validation and lean product design. Comfortable from low-level engineering to product strategy, he built fault-tolerant, 24/7 ML systems handling hundreds of thousands of decisions per day and mentored a junior engineer into sole platform ownership. A longtime open-source contributor, he’s improved statistical tooling (Incanter) and data-quality docs and tests (Great Expectations), reflecting a mix of production-grade engineering and practical data-science rigor. Based in Tokyo, he now explores how AI should augment human decision-making, blending hard operational experience with startup-scale experimentation.
15 years of coding experience
15 years of employment as a software developer
MASc, MASc at University of TorontoBASc, BASc at University of Waterloo
English, Chinese
Stackoverflow
1,759reputation
76kreached
12answers
23questions
Github Skills (29)
data-quality10
matrix10
clojure-cli10
statistics10
exploratory-data-analysis10
hadoop10
statistic10
clojure10
mat10
data-processing10
documentation10
data-analysis10
testing9
data-engineering9
cascading9
Programming languages (17)
Github contributions (5)
nathanmarz/cascalog
Feb 2013 - Dec 2014
Data processing on Hadoop without the hassle.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:93 commits in 1 year 10 months
Contributions summary:Paul primarily contributed to the development and enhancement of Cascalog, a data processing library for Hadoop. Their work involved adding new features, such as more tap options for data input/output, and refactoring existing code. They also made adjustments to the project's dependencies and test configurations, indicating involvement in project setup and maintenance. The user's modifications to the core functionality and testing of Cascalog suggest a focus on improving its capabilities and ensuring its reliability.
data-processingbig-datasparkhadoopjava
incanter/incanter
Aug 2011 - Dec 2012
Clojure-based, R-like statistical computing and graphics environment for the JVM
Role in this project:Back-end Developer
Back-end DeveloperContributions:59 commits in 1 year 4 months
Contributions summary:Paul primarily contributed to the `incanter/incanter` repository by refactoring and enhancing core functionalities, focusing on statistical computing and graphics. Their work involved modifying existing code, likely to improve efficiency or readability, as indicated by the "minor refactor" commit messages. Additionally, the user added new features, like `transform-col` for in-place column transformations and enabling the "sample" of the dataset, expanding the library's capabilities. Furthermore, they implemented Benford's Law test, thereby enhancing the statistical analysis features.
statisticaljvmcomputingclojuregraphics
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