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Siyun Liang has a diverse range of work experience in data engineering, data science, quantitative research analysis, and commodity research analysis.

In 2016, Siyun worked as an intern at China Construction Bank.

In 2017, Siyun gained experience as a student intern at the Chinese Academy of Sciences.

From 2020 to 2021, Siyun worked as a Commodity Research Analyst at Guangdong Shengen Holding Group, where they researched futures and options of rebar and iron, collected and organized industry data, and wrote daily reports.

In 2021, Siyun joined Ricequant as a Quantitative Research Analyst, focusing on analyzing indexes and convertible bonds. They accurately forecasted stock transfers based on the CSI 300 index compilation manual.

From 2021 to 2022, Siyun worked as a Research Assistant at the University of Southern California.

In 2022, Siyun joined RMDS Lab as a Data Science Project Impacter, where they improved feature comparison and data structure in a user recommendation system, optimized algorithms for training and sorting similarity scores, and conducted exploratory data analysis on a 30k NFT dataset.

Currently, Siyun works as a Data Engineer at Stable, utilizing Scrapy in Python to streamline the ETL system and optimize data pipelines for web scraping various data formats. They have improved efficiency by 80% using Python and MongoDB.

Siyun Liang completed a Bachelor's degree in Financial Mathematics at Jilin University from 2015 to 2019. Following that, they pursued a Master of Science degree in Mathematical Finance at the University of Southern California from 2019 to 2022. In July 2021, Siyun Liang obtained the Certified Financial Risk Manager (FRM) certification from the Global Association of Risk Professionals (GARP).