Career Profile
Analytical and detail-oriented Risk Analyst with a B.S. in Economics and a proven track record in credit analysis, data-driven risk assessment, and process automation. Skilled in using Python, SQL, and Tableau to create risk-scoring models, streamline ETL processes, and support decision-making with data visualization, enhancing risk management and operational efficiency.
Education
- Computer Science Relevant Course: Statistics, Algorithms and Data Structure, Full-stack Web Development, Java and Object-oriented Design
- Economics Relevant Courses: Microeconomics, Macroeconomics, Econometrics, International Trade, International Monetary Relations, Taxation
Experiences
- Automated the ETL process for extracting unstructured data from various sources, cleaning, transforming, and aggregating it with business logic, and loading it into a centralized data warehouse. Ensured data accuracy and regulatory compliance (SQL, Python, Pandas).
- Conducted in-depth financial analysis and created risk-scoring models, identifying key prospects, mitigating credit risk, and improving the accuracy of credit assessments (Python, Pandas).
- As a defacto portfolio manager, underwrote and presented credit recommendations with data visualization to support decision-making on merchant pricing and policy development, aligning with risk management and profitability objectives (Matplotlib, Seaborn, Tableau)
- Collaborated with cross-functional teams including Sales, Credit, Ops, and Senior Management to assess and mitigate credit risk.
- Employed advanced data analytics techniques using SQL and Python for comprehensive financial analysis and risk assessment.
- Performed extensive data analysis to guide critical credit evaluations and drive strategic credit decisions.
- Coached and mentored team members on spreadsheet functionalities, leading to enhanced operational efficiency and overall productivity.
- Analyzed and processed all incoming draws, reviewed documentation for proper approvals, and made loan disbursements to voucher control companies or borrowers in accordance with contracts.
- Conducted comprehensive reviews of loan documentation to verify accuracy, completeness, and compliance with policies and regulations, and presented at closings.
- Reduced the risk associated with global trade by reconciling the divergent needs of an exporter and importer.
- Developed strong working relationships with clients, brokers, title companies, and insurance companies to facilitate efficient loan processing and servicing.
Projects
Stock Trend Prediction Based on Historical Data
The goal of this research project is to implement, analyze and visualize multiple predicting technical indicators and factors to find out the essential factors affecting the stock change and forecast the future stock trend based on collected stock dataset.
- Collected, cleaned and preprocessed historical stock market data from Non-SQL database using Python.
- Built prediction models based on technical analysis and machine learning using Numpy and Scikit-learn.
- Analyzed the chosen stock using correlation analysis and risk analysis (Monte Carlo simulations).
- Compared the performance of prediction models by building the stock trading simulation pipeline.