Naien He is a Lead Data Scientist with 7–8 years of experience applying operations research, machine learning, and process systems engineering to industrial operations, currently driving end-to-end analytics and optimization at Linde. He has a strong track record delivering measurable business impact—from a 5% distribution-cost reduction via custom heuristics to award-winning productivity projects and a first-of-its-kind order-fulfillment analytics platform during supply-chain shortages. Comfortable across the stack, he builds ETL pipelines, real-time analytics, optimization and ML models, and production-facing frontends (Python, C#, JavaScript, Vue.js) while shaping architecture and developer workflows. His background in chemical engineering (CMU MS, HKUST BE) and early hands-on plant deployments gives him uncommon domain fluency for translating process chemistry and control data into scalable digital solutions. Colleagues rely on him to prototype high-leverage tools quickly and to operationalize models that reduce peak-energy penalties and improve resource allocation.
8 years of coding experience
4 years of employment as a software developer
Bachelor of Engineering, Chemical and Biomolecular Engineering, 3.6/4.00 First Class Honor, Bachelor of Engineering, Chemical and Biomolecular Engineering, 3.6/4.00 First Class Honor at Hong Kong University of Science and Technology
Master of Science - MS, Chemical Engineering, 4.0/4.0, Master of Science - MS, Chemical Engineering, 4.0/4.0 at Carnegie Mellon University
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