Iris (Huasi) Ou

Machine Learning, Deep Learning, Microsoft Azure, python, pyspark, SAS, R, JIRA

- ML Model Performance Analysis: Focused on machine learning credit risk models (e.g., logistic regression), conducting in-depth analysis of PSI, CSI, Gini Score, and Accuracy Ratio to optimize model inputs. - Automation & Efficiency: Automated model metric analysis, quarterly performance reports, and data visualization using python and VBA, resulting in a 70% reduction in processing time and increased flexibility for model oversight. - Statistical Application: Applied T-tests and F-tests to monitor year-over-year changes in features crucial for Probability of Default (PD) modeling, aiding in variable selection and explaining model performance. - PowerAI & Business Analytics: Utilized PowerAI and business analytics to enhance hiring precision through improved applicant-opportunity matching and automated return-to-office application workflows.

- Data Analysis & Visualization: Analyzed data for traditional and tokenized IPOs, using Python and Excel for data visualization to highlight the advantages of tokenized assets. - Digital Asset Market Research: Led primary and secondary research to analyze the digital asset market, instrumental in identifying potential customers and expanding market reach. - Statistical Methods Application: Deepened practical understanding and application of statistical methods and concepts in a fast-paced environment, supporting data-driven strategies.

- Efficiently arranged courses and classrooms, ensured smooth logistical operations for students and faculty. - Prepared comprehensive pre-class materials, ensured students were well-equipped for their learning. - diligently addressed student inquiries and questions with accuracy and thoroughness. - Provided helpful support by proactively answering student questions and ensuring a positive and informative experience. - Effectively engaged with the Chinese student community, built a valuable network and fostering a supportive environment.

- Data Generation for Analytical Models: Generated precise experimental data through ingredient quality analysis of medicines. This experience in meticulous data acquisition is used for training accurate models in areas like quality control and predictive analytics. - Automated Data Reporting for Efficiency: Designed and automated microscopic quality reports using Microsoft Word and Excel. The automating data processing and report generation are directly used to building efficient data pipelines for medicine applications. - Ensured Data Integrity for AI Reliability: Contributed to pharmaceutical quality control, emphasizing the critical importance of data precision and reliability. it ensures the trustworthiness and efficacy of medicine system.

The MFI is a professional program that provides students with a sophisticated understanding of this complex interaction of the financial and insurance fields. The program contains a comprehensive set of offerings and students gain rigorous training in data science, actuarial science and finance. Graduates from this program will be well armed to face the highly skilled work required of them in the banking, insurance, pension and consulting industries. Relevant Project: Probability of Default (PD) Model Development in Python • Developed a PD scoring model for credit revolving loan and prepared and presented a technical document model specification • Performed logistic regression and evaluated the model performance (AUROC, KS, AR, WOE, IV) and feature selection process

Dean’s Honor’s List York University Continuing Education Scholarship