S&P Global Ratings’ Model Implementation Center is responsible for establishing and maintaining quantitative excellence by developing and testing, cutting edge quantitative models and applications for internal use. Within the Model Implementation Center we are seeking an Associate, Quantitative Modeling in our New York office.
The ideal candidate will have practical, hands-on financial engineering or similar experience in the area of quantitative modeling and model testing. The candidate will be able to communicate effectively with quantitative analysts and model developers within the group as well as IT developers and technical experts within the various supported ratings businesses. The candidate will also work closely with rating analysts to understand and validate the implementation of models.
The scope of projects for model testing and implementation include rule based models as well as simulation or regression based models for credit risk assessment.
The person in this position will work in the following areas:
· Participating in the development, implementation and maintenance of high-performance quantitative financial models that meeting analytical needs using the following languages: MATLAB, Python, C/C++, Excel/VBA.
· Composing detailed quantitative model implementation specifications documents.
· Testing models built in various platforms. The tasks include, but not limited to, proposing testing plans, designing test cases, and enhancing test system implementations.
· Lead or support research on quantitative finance modeling using Python, MATLAB, R, SAS or other languages.
· Advanced degree (master or equivalent) in Financial Engineering, Quantitative Finance or a closely related field. Computer Science, Data Science and Engineering graduates will also be considered.
· Proficient programming in at least two of the following languages: C++, MATLAB, Excel/VBA, R, and Python
· Good written and oral communication and interpersonal skills.
· Experience in technical or scientific writing is a plus.
· Participation in CFA or FRM is a plus.
· Knowledge of time series analysis, stochastic process, data mining and advanced regression analysis is a plus.