About Me

My name is Hangjun Zhang, but you can call me Simon. I come all the way from Chengdu, China, the hometown of pandas! Currently, I obtained my bachelor's degree from Northeastern University, Major in Data Science and Economics.

This page details my academic background and professional experience. If you'd like to learn more about me, please click the button below.

Beyond Academicss

My latest projects

Education Background

Professional Experience

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Northeastern University

B.S. Data Science & Economics (2023–2026).

Core Courses:

Microeconomic Theory
Macroeconomic Theory
Advanced Applied Econometrics
Machine Learning / Data Mining I
Machine Learning and Artificial Intelligence
Game Theory
Foundations of Data Science
Database Design

Northeastern University London

B.S. Data Science & Economics (2022–2023).

Core Courses:

Discrete Structure
Business Statistics
Global Markets & Local Culture

Research Experience

Cross-Sectional Factor Modeling in China’s A-Share Market
Factor Performance Diagnostics and Multi-Factor Testing (A-Shares)

IQIYI

AI Innovation & Data Intern

Collaborated with a research team to develop two AI application services: AI customer service, which helps the customer service department reduce its workload by 40%, and AI cartoon production, which allows users to easily produce children's cartoons in less than three minutes.

Utilized Dify for prompt engineering and Midjourney/Stable Diffusion for generative tuning, incorporating 200+ knowledge entries to fine-tune LLMs, yielding 60% accuracy improvement in response quantification.

Used Python (BeautifulSoup, JSON, API) to collect user data from web pages/applications; conducted in-depth analysis of user data performance, and modify AI parameters.

First Plus Asset Management

Quantitative Researcher

Applied multi-factor risk model (Barra) to estimate time-varying idiosyncratic risk and covariance matrices for 5000+ securities, implementing rolling windows, data reshaping, time-series pivoting.

Implemented multiple covariance-matrix adjustments, such as Newey-West, Eigenfactor Risk Adjustment, volatility-regime adjustment, etc, to improve the stability and out-of-sample performance of risk forecasts.

Evaluated the effectiveness of each adjustment technique by analyzing bias statistics across single-factor portfolios, random portfolios, and GMV portfolios.

Natixis Investment Managers

Data Engineer

Analyzed revised trading data by Python (Pandas, Scikit-learn) to verify pattern of key indicators such as realized gain/loss and quantity via pre–post difference analysis; identify discrepancies against departmental assumptions and regress potential root causes.

Built a Redshift data-extraction pipeline in Python and SQL capable of processing millions of SQL queries, merging fragmented query segments, identifying patterns and real table usage (excluding aliases), and generating user-frequency summary tables to support data management and usage analysis.

Constructed and deployed QuickSight dashboards, such as the EIM Team Jobs Monitor, which enables real-time monitoring of ongoing projects and operational analytics.