Shuai Yuan

Quantitative Researcher Intern

About

I am an incoming student in the MS in Financial Economics program at Columbia Business School. I previously studied at UC Santa Barbara, where I built a strong foundation in modeling, probability, and machine learning. I’m now combining these technical skills with a growing interest in finance, aiming to apply data-driven methods to real-world challenges in quantitative trading and research. My passion for markets began with an unexpected entry point: trading player cards in FIFA Ultimate Team. The thrill of spotting mispricings and making profitable decisions based on market patterns stuck with me. Since then, I’ve led teams in global trading competitions, developed algorithmic strategies, and interned at a quant fund where I built predictive models for electricity prices. I’m driven by the challenge of turning complex data into actionable insights—and excited to keep pushing the boundaries of what data can reveal.

Country

United States

City

New York

Industry

Financial Services

Skill

Time Series Analysis, Time Series Forecasting, Anomaly Detection, Microsoft Word, SSH, Xarray, PCA, Data Classification, Algorithmic Trading, Exploratory Data Analysis, IT Audit, Microsoft Excel, Microsoft PowerPoint, Data Visualization, PyTorch, Python (Programming Language), SQL

Experience

Quantitative Researcher Intern

Beijing Genesis Investment Management Co., Ltd

2024-8 - 2024-11 · 4 mos

Beijing, China

Beijing Genesis Investment Management is a Beijing-based private equity firm focused on equity index enhancement, with growing initiatives in the electricity market and quantitative modeling. • Constructed neural network for multivariate time series anomaly detection in electricity markets. Combined CNN model capturing local patterns and RNN model extracting structures within sliding time windows, doubled benchmark f1-scor • Established models using climate, geographic, and energy data for long-term prediction on load and other supporting factors • Delivered temperature index features leveraging exogenous variables, improved model's MAE by 18% in prediction tasks • Formulated policy studies and strategy making for newly launched real-time electricity market in Shandong & Guangdong Province, created a hedging opportunity for customers with extensive power demands influenced by weather

Research Assistant

UC Santa Barbara

2023-9 - 2024-6 · 10 mos

Santa Barbara, California, United States

Data Science Capstone Project with Prof. Shraddhanand Shukla and Climate Harzard Center. Developed a cross-dataset validation framework that aligns spatiotemporal granularity and assesses consistency, bias, and predictive alignment among satellite-, reanalysis-, and model-based global precipitation estimates Quantified extreme-value concordance across datasets using event-based similarity metrics and lag-aware statistical testing Evaluated time-series predictability and causal structure between soil moisture and precipitation using Granger causality test Performed regional stratification analysis to examine dataset reliability under non-stationary conditions (eg. wet/dry seasons)

Data Analyst Intern

Ernst & Young

2023-7 - 2023-8 · 2 mos

Beijing, China

• Streamlined the SQL/Excel data analysis and visualization to output professional IT audit reports with minimal manual input • Resolved user experience issues in previous product videos by deep diving UI elements and produced a demo video for clients • Presented key findings in user experiences to senior management with demo, coordinating with 3 senior leaders and partner team • Developed 3000+ lines of SQL code with efficiency improvement, with code approved and incorporated into production system