Qinyang Huang

Raleigh, NC

919-***-**** ad2rnd@r.postjobfree.com https://www.linkedin.com/in/QinyangHuang/ Summary

Financial Mathematics graduate student with over 1.5 years of hands-on experience in data analysis as well as market trading. Skilled in data visualization, modeling, and machine learning, using software tools including Python, SQL, and Excel. Education

Global Association of Risk Professionals (GARP)

FRM Program - Passed FRM Exam Part I

Raleigh, NC

December 2024

Shanghai, China

July 2022

Technical Skills

Work Experience

Over-the-Counter (OTC) Trader, Investment Team

Shanghai Xuanling Asset Management Co., Ltd.

Shanghai, China

January 2022 - April 2023

• Oversaw daily subscriptions for newly listed stocks and actively engaged in the declaration and execution of off-exchange private placement projects, consistently yielding a 10% annual return. Data Analyst Intern, Asset Management Department

China Fortune Securities Company Limited

Shanghai, China

September 2021 - November 2021

• Extract major macroeconomic indicators, notably the Consumer Price Index (CPI) data from the Wind Database, and perform data cleaning, transformation, data quality check, and exploratory data analysis. University of Shanghai for Science and Technology

Bachelor of Business Administration

North Carolina State University

Master of Financial Mathematics (GPA: 4.0/4.0)

May 2021

Analytics: Python(3 years), SQL, Microsoft Excel, Tableau Platforms: Bloomberg Terminal, Wind Database

Financial Risk Analysis, Machine Learning, Financial Data Analysis with Python & R, Statistical Inference, Stochastic Calculus for Finance, Monte Carlo Methods for Financial Math, Options and Derivatives Pricing

• Extracted data from the Wind database and executed comprehensive data analysis, contributing to the development of a targeted arbitrage strategy for convertible bonds.

• Monitored market conditions to ensure compliance with internal guidelines and regulatory standards.

• Developed relationships with external brokers and vendors to optimize trading operations. Related Coursework

• Regularly reviewed pertinent financial news and distilled insights into concise summaries to keep the team updated on significant financial news.

• Performed in-depth data analysis, using linear regression models in Python to derive insights from large datasets for forecasting economic trends and impacts.

• Utilized a Freddie Mac single-family loan dataset with 500,000 cases, 27 features and the binary label from 2000 to 2018. Project Experience

Predicting Loan Defaults with XGBoost Model August 2023 - November2023

• Adopted multiple machine learning models such as Logistic Regression, Random Forest and XGBoost to predict default status. Used hyperparameter tunning to enhance model accuracy.

• Conducted comprehensive data processing, including the application of feature selection techniques like forward feature selection and LASSO. Addressed data imbalance using SMOTE and transformed categorical data into booleans through one-hot encoding.

• Achieved a high prediction rate with an F-1 score value of 0.93 and an AUC-ROC score value of 0.98.