Arthur Jiang
Go-to-Market Sr. Director, Food GC at 嘉吉公司
Shanghai, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolArthur Jiang is a commercial leader with 10+ years driving go-to-market, sales and strategy across food, manufacturing and consumer industries in Greater China and APAC. He has a consistent track record of building high-performing regional teams, expanding distributor networks, and exceeding targets (including 120% volume achievement and major margin improvement at Cargill). An MBA from IMD and technical degrees in chemical/materials engineering inform his ability to bridge commercial strategy and product/ingredient sourcing. Arthur also contributes to open-source back-end work for Microsoft’s MARO RL platform, showing hands-on data pipeline and business-engine integration skills not typical for senior sales leaders. Based in Shanghai, he combines charismatic people development with a performance-first, data-oriented approach to market expansion.
11 years of coding experience
5 years of employment as a software developer
Master of Business Administration (MBA), MBA, Master of Business Administration (MBA), MBA at IMD Business SchoolMaster, Chemical Engineering, Master, Chemical Engineering at Tianjin UniversityBachelor, Material Chemistry, Bachelor, Material Chemistry at Harbin Institute of Technology
English, Chinese, Chinese
Github Skills (8)
data-pipeline10
data-pipelines10
python10
data-processing10
pandas9
data-engineering8
reinforcement-learning8
simulator8
Programming languages (4)
Github contributions (5)
microsoft/maro
Jan 2020 - Jan 2021
Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:9 releases, 706 reviews, 151 commits in 11 months
Contributions summary:Arthur primarily contributed to the `maro/cli/data_pipeline/citi_bike.py` file, developing and refactoring the data pipeline for the Citi Bike scenario. Their work included cleaning, processing, and building binary data files from the original data source. They integrated weather data and built data structures. The user also worked on the business engine, configuring and preparing the environment's core logic.
multi-agentreinforcement-learningmulti-agent-reinforcement-learningsimulatorresource-optimization
ArthurJiang/config
Sep 2017 - Dec 2023
Contributions:18 pushes, 1 branch in 6 years 3 months
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