# Yuming Shi

> Software Engineer at Amazon Nimble Studio

Location: San Francisco Bay Area, United States
Profile: https://flows.cv/yumingshi

## Work Experience
### Software Development Engineer @ Amazon Web Services (AWS)
Jan 2021 – Present | Sunnyvale, California, United States

### Software Engineer @ Helios Data Inc.
Jan 2019 – Jan 2021 | San Francisco Bay Area
• Developed a datasource scanner to scan, classify, and normalize contents containing Personal Indentifiable Information (PII) from SQL-like databases and network filesystem. Optimized scanning workflow with an internal pipeline with go channels, which improved scan processing speed by 4 times.
• Designed and implemented a master-slave model for the scanning system to communicate with backend HTTP server  for request delegation and load balancing. Deployed scanner clusters as micro services using docker containers and gRPC, and scaled the system with distributed storage/message queue like consul, Cassandra, and Kafka. Each scanner node is capable of scanning 100k+ tables in each data source and up to 3 million rows in each sql table.
• Developed and maintained backend web servers using Gin-Gonic and Express/Node.js HTTP web frameworks. Designed and implemented RESTful backend APIs as well as internal database models using gorm/sqlalchemy and PostgreSQL. Improved query time to 10x faster by de-normalizing database schemas.
• Cooperated with data team to build a secured data-sharing platform with Spark. Instrumented and intercepted Spark process to monitor suspicious file operations on both local disk and HDFS with the help of java agent and Byte Buddy, which prevented important data like PII from leakage.
• Designed and optimized product features according to real-time feedback from customers during product POC and worked closely with product team to actively respond to feasibility verification requests at the same time. Concluded and provided solutions for on-site support engineers rapidly.

### Data Analysis Student Assistant @ Cisco
Jan 2017 – Jan 2018 | Raleigh-Durham, North Carolina Area
• Currently working on a multiple network failure type classification project. Datasets and scoring matrix will be used for a Cisco-owned data analysis competition on Kaggle
• Developed a time-series based predictive model in Python with open source data from incomplete data sets, ranking the first in the class with accurate predictions and highest responsivity
• Cleansed 500M raw data of network stream transportation, and clustered them according to node failure characteristics

### Student Research Assistant @ General Motors
Jan 2016 – Jan 2017 | Greater Detroit Area
• Coordinated teamwork on a global basis with GM lightening design department and manufacturers across the U.S., China, and Germany
• Presented research results in GM heritage center at Warren, MI and appreciated by the managing team of exterior lights
• Improved and applied hydrophobic surface with microstructure created by injection molding on headlight inner surface to prevent condensing

### Data Analyst Intern @ GE
Jan 2016 – Jan 2016 | Shanghai City, China
• Analyzed inventory storage information contained in raw EXCEL spreadsheets provided by the client. Cleansed the data and transferred to SQL Server with Python for further analysis
• Simplified data visualization process to analyze factory storage information based on existing data source with Tableau, and established a real-time inventory analysis platform by extracting data from remote SQL database
• Addressed current oversights of the inventory management system for the client in Vietnam, and drafted requirements for future periodically updated digital inventory stock database

### Research Assistant @ Shanghai Jiao Tong University & Sinopec
Jan 2016 – Jan 2016 | Shanghai City, China
• Improved current clustering method for spare parts used in the petrochemical enterprise by combining two scoring models, and increased the clarification rate of fast moving and slow moving spare parts
• Employed Bayesian algorithm to design a forecasting model of slow-moving spare parts with MATLAB, and predicted required inventory amount based on given confidence coefficient.
• Developed statistical cost function to forecast and optimize the economical spare part inventory amount, given inventory demand, stock-out cost and ordering cycle


## Education
### Master's degree in Engineering/Industrial Management
Duke University

### Bachelor of Science - BS in Mechanical Engineering
UM-SJTU Joint Institute, Shanghai Jiao Tong University


## Contact & Social
- LinkedIn: https://linkedin.com/in/yumings

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Source: https://flows.cv/yumingshi
JSON Resume: https://flows.cv/yumingshi/resume.json
Last updated: 2026-03-29