Risk Management Machine Learning

QIANYUE LU

+1-646-***-**** ******@********.*** New York, NY 10025

EDUCATION

Columbia University, New York, United States Sep. 2023 – Dec. 2024 Master of Science, Enterprise Risk Management (STEM) Relevant Coursework: Quantitative Risk Management, Coding for Risk Management, Financial Risk Management, Machine Learning for Risk Management, Real Estate Financing (MBA), Asset Management (MBA) Bayes Business School, London, United Kingdom Sep. 2019 – May. 2022 Bachelor of Science, Finance, Upper Second-Class Honours (GMAT 730) Relevant Coursework: Company Valuation, Corporate Finance, Mergers and Acquisitions, Intermediate Financial Accounting, Macro and Microeconomics, Financial Econometrics, Derivatives Trading and Hedging Strategies INTERNSHIPS

Marlin Equity Partners Remote Jun – Aug. 2024

Quantitative Risk Intern

Cleaned a decade-long daily loan activity dataset; converted categorical features to numerical, splitting data into training and testing sets, and optimizing model performance through strategic hyperparameter tuning

Developed predictive models including random forest, gradient boosting trees, and regularized logistic regression to forecast loan defaults; assessed model performance using deviance, ROC curves, and AUC scores with Python

Deployed the Brinson model to calculate YTD excess return for the portfolio (1.02 %) versus the benchmark

(0.75%); decomposed excess return into asset allocation, security selection, and interaction effects

Utilized pandas to process financial data, compute weighted returns, and perform complex attributions for a diversified ETF portfolio; delivered crucial insights into asset performance and managerial investment effectiveness HP Tech Ventures Remote Dec. 2023 – Feb. 2024

Venture Capital Externship

Conducted investment research and analysis on potential startup targets with Crunchbase; presented the insights to senior management through PowerPoint presentation, garnering positive feedback

Proficient in SQL for merging tables using LEFT JOIN and INNER JOIN; adept in data cleaning by integrating SQL functions like IF, COALESCE, REPLACE, and TRIM, enhancing data processing accuracy and efficiency

Conducted correlation and regression analysis on 300,000+ data points using Excel and Python’s Pandas and SciPy libraries, providing management with crucial insights into key factors influencing total funding amounts

Visualized data distribution with histograms and line charts in Tableau, underpinning data-driven decision-making Lions Financial New York, NY July – Sep. 2023

Risk Management Intern

Utilized balance sheet and cash flow statement to analysis and compare liquidity metrics such as current ratio, cash ratio, and CF/LF ratio; provided senior managers key information for strategic risk prioritization

Collected 3-year closed stock price data from Yahoo Finance and deployed Python NumPy and SciPy to execute a Value-at-Risk (VaR) market risk assessment, influencing senior management's investment decision-making

Leveraged Tableau to create an intuitive risk matrix and management team analytics dashboard; transformed complex data sets into actionable insights through data visualization techniques like pie chart EXTRACURRICULAR PROJECTS

Soft Commodities Portfolio Volatility Forecasting Dec. 2024

Developed Python-based volatility estimate models such as standard deviation, EWMA, AR, and GARCH for a

$100 million soft commodities portfolio, enhancing pricing forecast accuracy

Conducted risk analysis to define loss ranges and utilized time-series charts to illustrate portfolio trends, aiding strategic decisions for senior management

Optimized Neural Architectures for Image Classification Dec. 2024

Constructed diverse neural network architectures in MATLAB, including shallow, multi-layer, and bespoke models, employing various optimizers like Adam, SGDM, RMSProp, and NADM to efficiently balance accuracy and processing time

Evaluated network configurations to assess convergence and overall performance, revealing insights into the effects of network depth on training dynamics and optimization; guided the selection of the optimal neural architecture for practical deployment

OTHER INFORMATION

Technical Skills: MATLAB, Python, SQL, R, Microsoft Office (Excel VBA, Word, PowerPoint), Tableau Interests: Pandas, Mystery Novels, Whisky

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