Job Duties: Support, develop, assess, and execute statistical, mathematical, and financial models for loss estimation, risk assessment, financial valuation, and performance measurement. Use mathematical, logical and software based analytical tools to provide ongoing analysis of existing and new programs/products and provide insightful suggestions backed by quantitative analytical results or reports, provide deeper analysis of risk, pricing, cash flow forecast, or information needed to develop and refine various financial models.
- Conduct theoretical and empirical research using public and proprietary data in all areas of risk management, including mortgage products and securities, borrower behavior, investment and hedging strategies, residential property valuation, macroeconomic models including housing prices and interest rates, financial valuation of finance assets and derivatives, economic capital, and stress testing;
- Apply mathematical, statistical, and econometric techniques to provide innovative, thorough, and practical solutions to support business strategies and initiatives;
- Utilize data mining and statistical techniques to develop analytic insights and sound hypotheses;
- Develop a database by incorporating clients’ data into packaged database software and design worksheet and user interface;
- Conduct quantitative analyses, modeling, or programming using SQL, R, or Python;
- Produce data visualization and interactive outputs with packages in SAS or R
- Perform ongoing ad hoc data analysis or/and model analysis for specific clients;
- Identify opportunities to apply quantitative methods to improve business performance;
- Implement validation strategies and assess the quality and risk of model methodologies, outputs, and processes with some supervision;
- Apply understanding of relevant business context to properly interpret model results, monitor performance and assess risks;
- Provide guidance and coaching to junior team members on analyses and modeling projects;
- Communicate technical subject matter clearly and concisely, both verbally and through written communication, via white papers, reports and presentations, to individuals of various backgrounds.
- Master’s degree in Economics, Finance, Statistics, Finance, Applied Mathematics, or other related quantitative discipline.
- Advanced econometric analysis knowledge including panel data, time series, linear regression, logistic regression
- Solid knowledge of statistical and data mining techniques, including Machine Learning tools such as Gradient Boosting Machine
- Ability to handle large datasets using SAS, SQL, Matlab, STATA, and R
Please send your resume and transcript to email@example.com.