Yifan L.

Quantitative Modeling and Analytics | Senior Associate at PwC Mainland China and Hong Kong | IFRS9 | Valuation | Risk Management

Chaoyang District, Beijing, China

About

Quantitative Modeling | Financial Analysis | Valuation MS Finance at Simon Business School (STEM-Certified Program); Double major in Mathematics and Economics at University of Wisconsin Madison (STEM-Certified Program) Skills set: Technical: Python, R, SQL, SAS, Tableau, Advanced Excel, Machine Learning, Linear/Logistic/Lasso regression models, VBA, Adobe Suite Business: IFRS9, ECL model, Risk management model, Cash flow Analysis, Valuation, Forecasting, Ad-hoc Report, A/B Testing, Financial Modeling

Experience

  • Senior Associate at PwC Mainland China and Hong Kong
    Jan 2019 - Present · 7 yrs 6 mos

    Risk management Risk model Financial instrument/capital market Valuation IFRS 9 Expected Credit Loss model IFRS 9 valuation

  • Business Analyst at Arecy LLC
    Feb 2018 - Sep 2018 · 8 mos

    • Compiled data to generate financial statements and performed analysis using Excel, Tableau and SQL. • Reconciled accounts to the P/L, resolved discrepancies, and completed monthly close process. • Acquisition Model Development for Personal Loans including cleaning data, imputing missing value, checking population distribution, building logistic regression model and stepwise selection method and testing models.

  • Financial Consultant at Krypital Group
    Oct 2017 - Feb 2018 · 5 mos

    • Raised 37,500 ETH in private and public sale and maintained 55,000+ client relationships in ArcBlock project.

  • Business Analyst at University of Rochester Medical Center
    Jan 2017 - Jun 2017 · 6 mos

    • Gathered and cleaned data using SQL and Excel. • Built forecasting model to analyze current operations, trends and budgets. • Created a cost saving model using Excel (Pivot Tables and Formulas).

  • Risk Management Analyst Intern at The Export-Import Bank of China
    Dec 2015 - Sep 2016 · 10 mos

    • Conducted risk assessment and business performance analysis using financial and risk models. • Optimized models monthly. • Generated recommendations based on both quantitative and qualitative analysis using Python.