Credit risk analyst at JD Technology (Fortune 500), working on dynamic credit scoring, real-time lending decisions, and AI-driven fraud detection. Previously held quant analysis roles at Mingyi Investment Fund and CITIC Securities.
Master's in Finance (FinTech) from Central University of Finance and Economics. Core skills: Python quantitative modeling, fixed-income arbitrage analysis, machine learning–based signal generation, and streaming computation for real-time risk decisions.
Building dynamic credit scoring system with multi-dimensional user profiling — integrating consumption behavior, repayment history, and social network data. Implementing real-time credit decisioning via streaming computation, with multi-modal deep learning for fraud and default risk detection. Designing post-lending gradient risk monitoring to track repayment behavior changes and enable differentiated intervention strategies.
Analyzed "fixed-income-like" profit patterns by scraping event time-series data to assess profit potential and duration. Built Python quantitative models for event-driven, price-driven, and interest rate-driven arbitrage opportunities across fixed-income markets.
Assisted in bond issuance documentation, including prospectus drafting and data preparation. Built Wind data extraction scripts for bond and market data collection, supporting the underwriting process.
Managed client communications and product recommendations for insurance products. Prepared life insurance proposals and supported business operations. Collected customer feedback to optimize service workflows.
Led a team to model deep-sea fish schooling patterns using simulated annealing and dynamic collision particle algorithms. Designed sustainable fishing strategies that balance yield against ecosystem impact.
Mathematical ModelingBuilt personalized recommendation engine using collaborative filtering and user behavior analysis. Designed a novel multi-suppression ranking algorithm for hot-content optimization.
Recommendation SystemHandled feasibility analysis and market performance modules. Implemented backend algorithms and cross-platform system integration from prototype to deployment.
Full-StackFeel free to reach out for collaboration or conversation.