SILQ
QIAN WANG has three years of work experience in software engineering and machine learning engineering. In 2021, they began working as a Software Engineer at SILQ. Prior to that, they worked as a Software Engineer at Brix Labs from 2020 to 2021. From 2018 to 2020, they worked as a Machine Learning Engineer at DiDi, where they optimized the price system short-term model by predicting the user's order rate. They were involved in feature selection and building models, and applied XGB models on large data sets to predict user order probability. They also conducted feature engineering and used CTREE to divide data into several relatively homogeneous parts, and improved prediction accuracy by applying K-means on small cities user data.
QIAN WANG attended the University of Southern California from 2017 to 2020, where they received a Master's degree in Electrical and Electronics Engineering.
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SILQ
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SILQ’s core mission is to simplify global trade and create a cost-effective and sustainable supply chain that is accessible to everyone. Thinking about supply chain optimization from first principles, we believe that by getting early access to production/manufacturing data, we can make shipping more predictable. To do this, we’ve built a network of field operators who go to factories and gather production insights. We’ve also developed a novel way to ship by leveraging these insights. Finally, Silq's platform brings it all together into a seamless shipping experience. With Silq, our customers get certainty around their ex-factory dates, make the most of their container space, and ship on consistent sailing schedules. We’re investing in both technology and operations every single day. And with each innovation, we continue helping our customers ship without surprises.