Mike Yung
Product Data Science
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMike Yung is a product-focused data scientist with over a decade of experience applying causal inference, machine learning, and product analytics across consumer tech companies from Uber and Spotify to Google and Notion. He specializes in treatment-effect estimation, LTV and propensity modeling, and turning causal insights into product decisions—work exemplified by his contributions to the popular open-source CausalML project. At Google he helped shape Shopping AI, and at Spotify and Uber he built ML systems for content valuation and causal experimentation that directly informed monetization and retention. Based in San Francisco with a background in economics and formal data science training, he blends rigorous causal methods with product instincts to answer high-leverage business questions.
10 years of coding experience
10 years of employment as a software developer
Data Science Fellow, Data Science Fellow at Galvanize IncEconomics, Economics at University of Pennsylvania
English, Chinese, Chinese, Korean
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (11)
lift10
machine-learning10
lifting10
python10
modeling10
causal-inference10
scikit-learn9
scikit9
jupyter-notebook9
pandas9
xgboost9
Programming languages (1)
Github contributions (5)
uber/causalml
Jul 2019 - Jul 2020
Uplift modeling and causal inference with machine learning algorithms
Role in this project:
ML Engineer
ML EngineerContributions:76 commits, 19 PRs, 31 pushes in 1 year
Contributions summary:Mike's commits focus on the development of an example notebook for demonstrating and using causal machine learning algorithms. Their work involves generating synthetic data, calculating propensity scores, and applying various meta-learner algorithms such as S-learner, T-learner, X-learner, and R-learner, to estimate the average treatment effect (ATE). The commits highlight the usage of CausalML package features, including its ready-to-use learners with the scikit-learn/xgboost regressor class and its feature importance features.
causal-inferencemachine-learning-algorithmsmachine-learninguplift-modeling
yungmsh/mining_the_common_app
Nov 2016 - Jun 2017
Contributions:33 commits, 31 pushes, 1 branch in 7 months
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