Quantitative Analyst and Data Scientist Resume

Guoying (Stephanie) Li

· ******@********.*** · linkedin.com/in/guoying-li · 949-***-****

EDUCATION

Master of Arts, Statistics GPA 3.62

Columbia University, New York, NY February 2021

Relevant Coursework: Probability, Inference, Linear Regression Models, Statistical Modeling and Data Sci- ence, Math of Finance, Capital Markets and Investments Certi cate, Database Management

University of California-Los Angeles, Los Angeles, CA June 2019 Relevant Coursework: Database, MongoDB, Apache Spark, Java, Data Analysis Using Python, Tableau Bachelor of Science, Mathematics GPA 3.63

University of California-Irvine, Irvine, CA September 2017 PROFESSIONAL EXPERIENCE

Data Scientist / Quantitative Analyst November 2021 { Present UBS, New York, NY

• Develop and maintain 7 nancial models to forecast revenue under multiple scenarios, including statis- tical tests, sensitivity analysis, and stress testing of core earnings drivers. Implement the models using Python, Databricks, and PySpark.

• Translate research into production-ready code and optimize model performance through mathematical transformations and e cient data handling.

• Work with IT teams to manage version control in GitLab and productionize models.

• Incorporate business stakeholder expertise to enhance model accuracy and relevance. Equity Risk Intern June { November 2021

Balyasny Asset Management, New York, NY

• Worked directly with a Senior Quantitative Researcher and Risk Managers working on data analytics projects using Python/SQL/Excel for monitoring markets and risk measures to ensure Balyasny's funds operate within intended risk limits.

• Researched and delivered insights related to portfolio/ rm risk exposures, portfolio construction, and quantitative analysis of the investment process. Applied risk parameters and calculations for earnings performance research and prepared for earnings ndings for discussion with the Chief Risk O cer.

• Improved framework for models related to analyzing risk, portfolio construction, investment process, and trading.

Data Analyst/Graduate Research Assistant February { June 2021 Columbia University Department of Statistics, New York, NY

• Participated in research projects on high frequency trading using WRDS and TAQ data sets focusing on mixed semi-martingales, speci cally detecting hidden fractional processes at high frequency and with the goal of improving estimator to obtain a smaller asymptotic variance.

• Worked directly with professors and PhD students working on data analytics projects using Python/R for exploratory data analysis, modeling, visualization, performance evaluation and report generation. PUBLICATIONS

When Frictions Are Fractional: Rough Noise in High-Frequency Data January 2025 Carsten H. Chong, Thomas Delerue, Guoying Li

10.1080/01621459.2024.2428466

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