Yingchuan Li Email & Phone Number | Experienced Machine Learning Engineer/Applied Scientist with 10+ years of relevant industry experience. Proficient in driving cross-functional collaborations and have successfully developed and deployed production models for search ranking, advertisement, recommendation systems, and logistic planning in the e-commerce domain. Well-versed in designing and implementing various DNN architectures for modeling works in recommendation system (both retrieval and ranking). Experienced in NLP techniques and related DNN architectures (RNN, Attention-based, and Transformers) and training/fine-tuning Large Language Models (LLM). Skilled in causal inference methods, particularly uplift modeling, to analyze treatment effects and optimize interventions for personalized recommendations. Sharpened problem solving ability through extensive research experience in both industry and academia (19 peer reviewed publications with 1k+ citations).

Yingchuan Li

Experienced Machine Learning Engineer/Applied Scientist with 10+ years of relevant industry experien...
Bellevue, Washington, United States

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Yingchuan Li’s Location Bellevue, Washington, United States

Yingchuan Li’s Current Industry Amazon

Yingchuan Li’s Prior Industry Goldman Sachs | Brookhaven National Laboratory

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Applied Scientist

2017-01-01 00:00:00 - Present

Quantitative Strategist

2013-01-01 00:00:00 - 2016-01-01 00:00:00

Research Associate

2010-01-01 00:00:00 - 2012-01-01 00:00:00

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Yingchuan Li is a Experienced Machine Learning Engineer/Applied Scientist with 10+ years of relevant industry experience. Proficient in driving cross-functional collaborations and have successfully developed and deployed production models for search ranking, advertisement, recommendation systems, and logistic planning in the e-commerce domain. Well-versed in designing and implementing various DNN architectures for modeling works in recommendation system (both retrieval and ranking). Experienced in NLP techniques and related DNN architectures (RNN, Attention-based, and Transformers) and training/fine-tuning Large Language Models (LLM). Skilled in causal inference methods, particularly uplift modeling, to analyze treatment effects and optimize interventions for personalized recommendations. Sharpened problem solving ability through extensive research experience in both industry and academia (19 peer reviewed publications with 1k+ citations).