Quantitative dev, research, Math, C++, Python

Linda Zhao

917-***-**** — # *****.****@***.*** — ï linkedin.com/in/Linda

Education

New York University Sep 24 – May 26

M.S. in Financial Engineering GPA:3.7

New York University Sep 20 – May 24

B.S. in Mathematics GPA:3.8

Minor: Computer Science

Experience

Quantitative Developer & Research Intern

Hermes Capital Advisors, LLC, New York June 25 – Sep 25

• Built a low-latency, event-driven data pipeline in C++ for ingesting high-frequency structured market data via the Databento API, simulating the full lifecycle of market operations from tick reception to downstream execution.

• Researched and deployed a deep learning classifier with a real-time inference pipeline, achieving 57% live test accuracy in predicting short-term market movements.

• Implemented high-throughput message handling with lock-free queues, cache-efficient data structures, and custom matching logic, reducing processing latency and supporting accurate real-time analytics.

• Designed modular frameworks for standardized feed parsing, deterministic data reconciliation, and signal validation, enabling extensibility across simulation, backtesting, and live trading contexts.

• Deployed a real-time inference engine that streamed live data into the model and routed forecasts back into the trading pipeline; integrated performance logging and P&L attribution to evaluate alpha quality. Quantitative Risk Modeling Intern

China Merchants Bank, China Jul 24 – Sep 24

• Developed and calibrated a multi-factor equity risk model to decompose portfolio returns into market, sector, and style exposures, providing senior management with a clearer view of portfolio risk drivers.

• Ran cross-sectional regressions across hundreds of equities to estimate factor returns and identify concentration risks, directly supporting portfolio rebalancing and hedging decisions.

• Constructed the factor covariance matrix from large-scale equity data, ensuring consistent and reliable risk estimates for daily portfolio monitoring.

• Conducted scenario analyses to evaluate the impact of macroeconomic shocks (e.g., interest rate hikes, sector rotations) on equity portfolio performance and capital allocation. Quantitative Analyst Intern

Global AI, New York Feb 24 – May 24

• Implemented Monte Carlo simulations under the Black–Scholes framework in Python to price Asian options; applied control variates and antithetic variates to achieve 20% variance reduction and 15% faster convergence.

• Benchmarked analytical models (Turnbull–Wakeman, Milevsky–Posner) against Monte Carlo, demonstrating improved computational efficiency ( 10%) and pricing accuracy ( 8%).

• Optimized simulation pipeline through vectorization and efficient memory management, reducing runtime by 25% on large-scale path simulations.

Teaching Assistant and Guest Lecturer

New York University, New York Sep 24 – May 25

• Delivered guest lectures on SVD and PCA for dimensionality reduction, combining theoretical exposition with hands-on Python demonstrations.

• Led weekly recitations for Calculus, Real Analysis, and Probability, covering topics such as Itˆo’s lemma, Brownian motion, binomial trees, and Monte Carlo simulation in the context of option pricing and stochastic modeling.

• Provided one-on-one academic support to diverse student audiences through holding office hours. SKILLS& CERTIFICATION

Skills: Python, Pandas, Numpy, Sklearn, C++, SQL, Excel(Pivot table), Power BI, HTML, PHP, JavaScript, R, Data Structure&Algorithm, Databases, Machine learning, Object Oriented Programming, Probability&Statistics Financial Engineering:Derivatives, Quantitative Methods in Finance, Valuation for Financial Engineering, Financial Risk Management

Certificates: FRM part1; Bloomberg Certified

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