Yechan Kang is a data scientist based in Sydney with 8 years’ experience turning complex datasets into revenue-driving products and decisions. With a PhD background in civil engineering and hands-on work across AWS, GCP, Snowflake and MLflow, he leads end-to-end ML lifecycles—from feature engineering on public census and transaction data to production deployment and MLOps. He has delivered measurable business impact, including a 16% uplift in membership through SageMaker models and improved portfolio revenue forecasts via demand-model updates and pricing elasticity analysis. Comfortable with stakeholder-facing roles, he runs cross-functional teams, presents to mixed audiences, and translates technical work into commercial outcomes. An occasional C++ contributor to the CMS Offline Software project, he also brings rigorous scientific training and a systems mindset to production analytics.
8 years of coding experience
Bachelor's degree, Civil Engineering, Bachelor's degree, Civil Engineering at Tongji University
Master's degree, Civil Engineering, Master's degree, Civil Engineering at Karlsruhe Institute of Technology (KIT)
Doctor of Philosophy - PhD, Civil Engineering, Doctor of Philosophy - PhD, Civil Engineering at Monash University
Contributions:6 reviews, 100 commits, 41 PRs in 3 years 9 months
Contributions summary:Yechan primarily addressed bug fixes and implemented minor improvements within the CMS Offline Software repository. Their work involved modifying C++ code, specifically focusing on plots and data validation within the muon detector system. They also introduced the use of GEMDigiSimLink for the matching of simulation data, and made changes to the naming scheme, also considering GE0 in GEM validation plots.
Contributions:1 PR, 82 pushes, 10 branches in 2 years
testbeam
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