People / Students / Class of 2027
Wangying Zhang graduated from the University of Nottingham with a B.S. in Statistics, studying across both the Ningbo China and UK campuses. She built a solid foundation in probability, optimization, and stochastic modeling. Proficient in Python, R, and SQL, she has gained cross-sector experience across automotive analytics, banking risk control, and quantitative finance.
During her internship at NIO, she analyzed over 400 vehicle telemetry signal points, built interactive dashboards for user experience optimization, and supported the development of a vehicle lifecycle ROI platform to drive data-informed product iteration. At Agricultural Bank of China, she designed a 12-indicator loan risk monitoring system and constructed credit scoring models using logistic regression to reduce non-performing loan exposure. On the research side, Wangying applied machine learning algorithms, including Random Forest, XGBoost, and SHAP interpretability analysis, to mine alpha factors for quantitative investment. She also engineered end-to-end trading systems integrating LLM sentiment embeddings with LSTM models, and contributed to AI-powered diagnostic infrastructure for rare diseases.
With technical experience in PyTorch, Scikit-learn, Tableau, and Power BI, Wangying is passionate about combining statistical theory and machine learning to translate complex datasets into actionable business insights.
