Ping Lo
Technical Support Engineer at Uber
New Taipei, Taiwan
Summary
🤩
Rockstar🎓
Top SchoolPing Lo is a Technical Support Engineer with 12 years of cross-functional experience bridging API integrations, enterprise support, and data-focused engineering. Based in New Taipei, Taiwan, he currently provides L3 Merchant POS API support for Uber Eats across APAC, serving major clients like McDonald's and Starbucks and acting as the primary Japanese-speaking escalation engineer. His background spans full-stack development and PL/SQL work in regulated financial systems, hands-on sales and B2B account management, and operational improvements that measurably reduced ticket-to-trip ratios in Japan. An active contributor to open-source ML tooling, he improved uplift-tree visualizations in the popular causalml repo to make causal insights more interpretable. Known for combining customer-facing empathy with technical rigor, he also brings a data-science curiosity that informs pragmatic automation and analytic solutions.
11 years of coding experience
3 years of employment as a software developer
Bachelor's degree, Business Administration and Management, General, Bachelor's degree, Business Administration and Management, General at National Taiwan University of Science and Technology
English, Japanese, Chinese
Stackoverflow
6,090reputation
914kreached
177answers
9questions
flask-sqlalchemy
top-5%
flask
top-5%
java
top-5%
python
top-5%
sqlalchemy
top-5%
Github Skills (21)
data-visualizations10
python10
machine-learning10
data-visualisation10
lift10
cython10
data-visualization10
lifting10
modeling10
causal-inference9
java9
scikit9
flask-sqlalchemy9
scikit-learn9
sqlalchemy9
Programming languages (7)
Github contributions (5)
uber/causalml
Sep 2019 - Sep 2022
Uplift modeling and causal inference with machine learning algorithms
Role in this project:
ML Engineer
ML EngineerContributions:8 releases, 103 reviews, 89 commits in 3 years
Contributions summary:Ping primarily focused on enhancing the visualization capabilities of uplift trees within the causalml library. They addressed a bug in the tree plot, ensuring edges connected to the correct parent nodes. The user improved the tree plot by adding information about sample proportions and implemented a color-coding system to represent positive and negative uplifts, enhancing the interpretability of the model. Furthermore, they contributed to example documentation, specifically for feature interpretations and meta-learners.
fairness-mldeep-learningmachine-learning-algorithmscausalinference
paullo0106/amazonsearch
Jun 2014 - Jun 2019
Contributions:11 commits, 8 pushes, 1 comment in 5 years 1 month
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