Summary
Yue Z is a data scientist with 12 years of experience melding finance, analytics, and production ML to improve decision-making and system performance. Trained at Columbia in Applied Analytics and with financial engineering coursework, she has built pricing and forecasting models that drove $12M in annual revenue uplift and operational ML pipelines used at Equifax and the NYSE. At NYSE she led latency and capacity analytics—creating end-to-end monitoring, Snowflake-driven ETL, and Python automation that supported real-time trading and risk mitigation for market makers. More recently she developed LLM-powered chatbot tooling and rigorous evaluation pipelines for the Social Security Administration, pairing deterministic routing with adversarial test datasets and execution safeguards. Comfortable across R, Python, SQL, Snowflake, and production Linux environments, she bridges quantitative modeling and engineering to deliver reliable, auditable systems. Colleagues describe her as pragmatic and results-focused—action-oriented beyond theory, as her GitHub motto suggests: action speaks louder than words.
12 years of coding experience
3 years of employment as a software developer
Bachelor's degree (B.S.), Finance, General, 3.65, Bachelor's degree (B.S.), Finance, General, 3.65 at State University of New York College at Oswego
Master's degree (M.S.), Applied Analytics, Master's degree (M.S.), Applied Analytics at Columbia University in the City of New York
Financial Engineering (Graduate-level Coursework), Financial Engineering (Graduate-level Coursework) at Stevens Institute of Technology
English, Chinese