Data Analyst Python

Ji Chen

Cell: 607-***-****

E-mail: **.********@*****.***

High-performing financial engineering graduate with quantitative model development, data science and risk analysis experience, prepared to assume roles in Quantitative Research, Data Scientist, Quantitative Modeling, Quantitative Analyst, etc. The detail-oriented mindset with the tireless work ethic and eagerness to design and deploy good risk and control practices. Strong technical background in quantitative modeling and programming, quantitative research, model validation, risk analysis, data science and data management. SKILLS SUMMARY

• Technical Skills: SAS, Python (NumPy, Pandas, Scikit-Learn, Matplotlib, etc.), R, SQL, VBA, MATLAB, Excel

(Advanced), MySQL, Oracle, Sybase, SQL Server, Tableau, Qlik Sense/Qlik View

• Quantitative Analysis/Data Science: Statistical/Econometrics/Risk Modeling, Data Science, Machine Learning, Model Testing, Model Validation, Quantitative Risk Management, Regression Analysis, Time Series Analysis, Monte Carlo Simulation, Stochastic Calculus, Data Management, Data Visualization, etc. PROFESSIONAL EXPERIENCE

UBS, Quantitative PPNR Modeler, New York, NY Oct 2018 – Present

• Build and develop quantitative predictive (statistical and econometrics) models by SAS, Python and R to forecast revenue/profit, balance across the company for various businesses and products (Global Wealth Management, Investment Banking, Asset Management) use in the Internal Forecasting and Business Strategy Decision, Stress Testing, Risk Management, Comprehensive Capital Analysis and Review (CCAR) and Pre-Provision Net Revenue

(PPNR) exercises and 3-Years Financial Planning (3YFP).

• Design machine learning algorithm for data collection and data analysis using SAS, Python, SQL, and Excel to ensure data quality for model development and accurate calculation results for the various lines of business; Classify data categories for model estimation, development and identify lists of potential risk drivers for statistical testing.

• Perform predictive analysis, model testing, model recalibration, model enhancement, back-testing, stress testing, scenario analysis and sensitivity analysis via model development, model remediation and model implementation circle by SAS, Python and Excel.

• Present model research and development processes, model forecast results to the various lines of stakeholder, business and finance and drive business decisions.

• Maintain a clear documentation trail of models approach and development process; improve model intelligence by liaising with internal and external resources; remain abreast of modeling research and development.

• Mentor and guild 3 analysts for model confirmation concept, model remediation, model implementation. Citigroup, Quantitative Financial Business Analyst, Warren, NJ Oct 2017 – July 2018

• W2 employee at Citigroup as Quantitative Financial Business Analyst (Payroll via IRIS Software).

• Developed wholesale credit risk models e.g. Probability of Default (PD), Loss Given Default (LGD), Exposure At Default (EAD) by SAS, R and Python to forecast credit loss and risk analytics for Loan and Debt Securities use in Comprehensive Capital Analysis and Review (CCAR), International Financial Reporting Standard (IFRS 9), Current Expected Credit Loss (CECL), Global Systematic Stress Test (GSST).

• Performed model enhancement, model testing, model recalibration, model implementation, back-testing, stress testing, scenario analysis and model monitoring by SAS and Python; Structured comprehensive model documentation to meet internal and regulatory standards.

• Created and maintained functional requirements documents, minor development documents, model development document and other business analysis artifacts, coordinated activities of technology teams in various organizations within the bank during project development.

• Designed test suites run on UAT & SIT testing platform, covered each individual component of the system, as well as, interfaces between the component and end-to-end workflow on project validation.

• Supported various mandatory firm-wide activities – continuity of business testing, for example, conduct project team meetings, analyze large datasets using Excel and SQL and other tools such as SAS, and Python to ensure data quality and model accurate calculation results, and break down business requirements into functional solutions. Investors Bank, Quantitative Risk Management Intern, Iselin, NJ June 2017 – Aug 2017

• 2017 Risk Management Summer Internship at Investors Bank.

• Enhanced, validated and maintained risk models including the Probability of Default (PD), Loss Given Default

(LGD), Value at Risk (VaR), Stressed Value at Risk (SVaR), and performed stress testing, back-testing, scenario analysis and model testing by SAS and Python.

SANOFI, Data Analyst & SAS Programmer Intern, Bridgewater, NJ May 2016 – Aug 2016

• 2016 Graduate Summer Internship at Sanofi Genzyme (Payroll via PRO Unlimited). EDUCATION

Master of Science in Mathematical Finance May 2017 RUTGERS UNIVERSITY New Brunswick, NJ

• Coursework: Mathematical Finance, Quantitative Risk Management, Regression Analysis, Time Series Analysis, Machine Learning, Derivatives Pricing, Numerical Analysis, Stochastic Calculus, Database System, Programming Finance and Computational Finance, etc.

B.S. in Economics, B.A. in Math (Actuarial Sciences), Double Major May 2015 SUNY-BINGHAMTON UNIVERSITY Binghamton, NY

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