Python, MySQL, R, Power BI, Tableau, Matlab, Microsoft office

Yueqi Wan

Los Angeles, CA 323-***-**** *****.***@*******.***

EDUCATION

University of Southern California Los Angeles, CA

Master of Science in Financial Engineering (STEM) 08/2022-05/2024

• GPA: 3.8/4.0

• Coursework: Data Mining, Machine Learning for Data Science, Stochastic Process, Hedge Funds, etc. Huaqiao University Quanzhou, Fujian, China

Bachelor of Economics in Finance 09/2018-06/2022

• GPA: 3.8/4.0

SKILLS

• Programming & Database: Python, R, MySQL.

• Data Science & Machine Learning: Hypothesis testing, A/B Test, Generalized Linear Models (Linear/Logistic Regression), Tree-Based Models (Decision Tree with Pruning, Random Forest), KNN, Clustering, PCA, etc.

• Visualization: Tableau, Power BI.

• Certifications: CFA Level 1 Passed.

INTERNSHIP

Data Analysis Intern - Probe Capital, Hangzhou, Zhejiang, China 05/2023-08/2023

• Conducted data analysis on specific subsectors (biopharmaceuticals, digital health services, etc.) in the healthcare industry to assess the investment trends using Vlookup and pivot tables.

• Performed top-down analysis on Protein Reagents sector in terms of industry fundamentals, market research, competitive landscapes, and valuation model construction (DCF model) to compile an industry research report. Quantitative Intern in the Derivatives Trading Department - China Securities Co., Ltd., Beijing, China 12/2021-01/2022

• Accomplished the portfolio optimization of commodity futures using Python.

• Scraped the daily data of major commodity varieties for the past 10 years using Beautifulsoup.

• Allocated assets through multiple methods, covering risk minimization, sharpe ratio maximization, risk parity, and correlation parity using Pandas and Numpy. And finally achieved a 1.5x Sharpe ratio improvement out-of-sample.

• Visualized the performance of optimization methods and validated their robustness. Intern of the Operation Department - Causis Investment, Wuhan, Hubei, China 07/2021-09/2021

• Supported in the operation of financial products, including product issuance filing, dividends, and liquidation with Microsoft Excel and Word.

• Disclosed and submitted the monthly, quarterly, and annual reports of funds on various platforms.

• Helped clients with redemption business, such as material delivery, recovery, and archiving. Financial Advisor Intern - Industrial Securities Co., Ltd., Wuhan, Hubei, China 07/2020-08/2020

• Participated in professional training encompassing Industrial Securities’ asset custody and outsourcing integrated services, private equity fund issuance and operation processes, etc..

• Assisted clients with the account opening and closing process, organized clients information data, calculated the daily settlement of Xingquan Green Investment Fund’s sales information using Vlookup in Microsoft Excel. ACADEMIC EXPERIENCE

Credit Risk Model Development 01/2024-02/2024

• Built a Credit Risk Model using Python to calculate the expected loss for the entire loan portfolio by combining PD, LGD, and EAD models using a dataset of over 800,000 consumer loans data.

• Conducted extensive data preprocessing, including creating dummy variables using Weight of Evidence (WOE) and coarse classing, dealing with missing values, removing insignificant input variables based on Information Value (IV) and p-values.

• Developed the PD, LGD and EAD model using a combination of Logistic Regression and Linear Regression, and integrated these 3 models to calculate the total Expected Loss for the whole portfolio of loans that the bank holds.

• Designed and implemented a scorecard system for individual borrower assessment.

• Model Validation and Maintenance: Evaluated model performance using metrics such as accuracy, ROC curve, AUC, Gini and Kolmogorov-Smirnov statistics. Monitored model stability using the Population Stability Index (PSI). Identification of Frost in Martian HiRISE Images 11/2023-12/2023

• Developed a classifier using Python to distinguish Martian terrain images with frost using NASA's dataset.

• Pre-processed image data and organized into train, test and validation data set.

• Trained a CNN + MLP model using image augmentation techniques, softmax function, batch normalization, L2 regularization, and ADAM optimizer. Trained for 20 epochs and performed early stopping using the validation set.

• Utilized transfer learning with three pre-trained models and compared results with the CNN + MLP approach.

• Reported and compared model performance using metrics including Precision, Recall, and F1 score. Financial Fraud Detection and Risk Analysis 01/2023-02/2023

• Developed a machine learning model using Python to predict fraudulent activities in financial transactions.

• Performed data analysis on over 100,000 transaction records, conducting data preprocessing tasks such as handling missing values, deriving additional features, encoding features, and addressing imbalanced label data using SMOTE.

• Built a prediction system based on Logistic Regression and Random Forest models, optimized model hyperparameters using GridSearchCV, evaluated model performance through 10-fold cross-validation, and ultimately selected the best-performing model based on evaluation metrics such as F1 score and AUC.

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