COMPANY PROFILE:
Standard & Poor’s Ratings Services, a division of McGraw Hill Financial, is the world’s foremost provider of independent credit ratings, indices, risk evaluation, investment research, data, and valuations. An essential part of the world’s financial infrastructure, Standard & Poor’s has played a leading role for more than 150 years in providing investors with the independent benchmarks they need to feel more confident about their investment and financial decisions. And we do it with utmost integrity. If our values speak to you, then speak to us.
Standard & Poor’s Quantitative Analytics Group is responsible for establishing and maintaining quantitative excellence by developing cutting edge quantitative research, models and applications for internal use and external commercialization. Within the Quantitative Analytics Group we are offering the following opportunity:
Intern – Quantitative Model Implementation-BEIJING CITY
The Quantitative Model Implementation is a combined effort involving model research, validation, numerical algorithm design, and implementation. The objective of the work is to create a set of model libraries that will be used by our products externally and rating business and research internally. The typical models would be stochastic interest rate and FX (CIR, HJM), house pricing index simulation, credit scoring, credit migration, fixed income valuation and spread models, etc.
The intern selected will become a member of quantitative model development team who are mainly responsible for implementing quantitative model libraries (in C++) and Matlab tools. The intern will work closely with senior researchers, quantitative developers and business analysts to understand the research models and implement them under our current infrastructure. The work also requires producing high quality model documentation, designing fast numerical algorithms in model computation and validating model implementations.
Qualifications:
Student in a discipline pursuing an advanced degree in Financial Engineering, Quantitative Finance, or Statistics with applications in the financial markets
Advanced quantitative modeling skills
Excellent Matlab and C++ programming skills and knowledge of SQL
Excellent communication and interpersonal skills
Knowledge of MS Office, including Excel and Power Point
Must be a self-starter, able to work independently, able to overcome obstacles, be detailed oriented, and think creatively
Intern – Quantitative Financial Quality Assurance-BEIJING CITY
The ability to produce testing plans, case libraries and systems to ensure high-quality quantitative financial model implementations requires both quantitative modeling knowledge and financial software testing skills. The Quantitative Financial Quality Assurance intern position offers extensive hands-on experience in the area of quantitative financial model research, implementation and testing.
The intern selected will be mainly responsible for implementing test case designs and maintainable test systems (in C++ and Matlab), producing model documents, and possibly integrating and consolidating multiple similar models. The intern will work closely with senior quantitative analysts and business analysts to understand and validate the implementations for models such as capital, reserve, stochastic interest rate and FX (CIR, HJM), house pricing index simulation, credit scoring, credit migration, fixed income valuation and spread models.
Qualifications:
Student in a discipline pursuing an advanced degree in Financial Engineering, Quantitative Finance, or Statistics with applications in the financial markets
Advanced quantitative modeling skills and ability to produce high quality documentation for models
Excellent C++, Matlab, and Excel/VBA programming skills
Excellent communication and interpersonal skills
Knowledge of MS office, including Excel and Power Point
Must be a self-starter, able to work independently, able to overcome obstacles, be detailed oriented, and think creatively
申请方式:
简历请发送至邮箱:windy.wu@mhfi.com。
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修改:GallifreyTD FROM 24.23.195.*
FROM 24.23.195.*