Supply Chain Summer Intern

Chunjie Gu

**** ******* ******, ***, ** ***** ******@********.*** 805-***-**** linkedin/in/chunjie-gu-12a781229 EDUCATION

Columbia University New York, NY

M.S. in Operations Research December 2023

● Coursework: Applied Analytic, Simulation, Stochastic Modeling, Supply Chain Modeling, Deep Learning for Operations Research and Financial Engineering

University of California, Santa Barbara Santa Barbara, CA B.S. in Financial Mathematics and Statistics June 2022

● Honors: Dean’s Honor (Winter 2020, Winter 2021, and Spring 2021)

● Coursework: Statistical Modeling, Quantitative Analysis, Financial Mathematics, Financial Modeling SUMMARY OF SKILLS

● Soft Skills: Problem-solving, Communication & Teamwork, Logical Thinking & Reasoning

● Hard Skills: Microsoft Office Suite, Python, R, SQL, MySQL, Excel, VBA, Bloomberg, Tableau, Power BI PROFESSIONAL EXPERIENCE

CITIC Wealth Management Corporation Limited Shanghai, China Summer Intern - Wealth Management department, Fixed Income Line July - August 2023

● Conducted extensive quantitative analysis utilizing advanced statistical methodologies, such as linear regression, within Python, to assess the performance of convertible bond portfolios.

● Identified and quantified significant multicollinearity issues, pinpointing key variables exhibiting high Variance Inflation Factors, particularly those pertinent to bonds, thereby enhancing investment strategies.

● Applied data-driven criteria encompassing duration, downside risk, volatility, and yield for the meticulous selection of short-term and medium/long-term pure bond funds across various timeframes.

● Vigilantly monitored designated portfolios, facilitating the acquisition of a 300M Chinese Yuan investment in a selected fund by delivering timely and effective bond analyses to the manager. CITIC Securities Hangzhou, China

Summer Intern - Institutional Clients department, Bond Line August - September 2021

● Formulated and delivered tailored capital structures for the New Energy Vehicle Business, with a specific emphasis on Lithium Battery technologies.

● Conducted comprehensive fundamental analysis of the electric vehicle battery industry's complete value chain, spanning Mineral Resources, Lithium Battery Materials, Lithium Battery Manufacturing, Key Components, and Vehicle Manufacturing for New Energy Vehicles, utilizing Excel.

● Fostered and oversaw strategic partnerships with prominent financial institutions in the Zhejiang area, nurturing collaborative relationships conducive to market insights and opportunities.

● Orchestrated a 30M Chinese Yuan bond issuance, effectively aligning the financing requirements of partner institutions with available financial solutions, thereby facilitating prompt capitalization. RESEARCH EXPERIENCE

Anomaly Detection for Wellington Management (Industry project) September - December 2023

● Conducted anomaly detection on credit transaction data for 48 companies, identifying high-interest rate regimes and Covid-19 pandemic anomalies, and presented the progress with the Wellington team biweekly.

● Validated and tested existing models given by the Wellington team, including but not limited to Spectral Residual Model,Prophet and STL assisted Model, and Multiplicative Decomposition model, determining Spectral Residual as the best-performing model in Python. NYC House Research, Columbia University November - December 2023

● Manipulated spatial data from NYC open data on 311 complaints and the 2015 tree census.

● Created and analyzed a database to examine relationships between NYC zip codes' rent levels, complaints, and tree counts in SQL.

JP Morgan Chase & Co. Quantitative Research Virtual Experience Program on Forage September 2023

● Conducted a gas price prediction simulation using regression in Python using data from Oct 2020 to Sep 2024 (data partially generated from another model); Utilized logistic regression to analyze a loan portfolio and estimate expected losses attributed to customer default probabilities.

● Employed maximum likelihood estimation to categorize FICO scores and predict defaults in Python

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