Quantitative Operations Manager

The Collateral Valuations team uses economic reasoning along with computational and statistical methods to analyze very large data sets in order to develop and manage models associated with the valuation of residential real estate.

In this role, the associate will be responsible for interpreting analysis, leading and managing modeling efforts, and be actively involved in the documentation and governance of multiple model systems. The expectation is that a candidate will possess the ability to create sophisticated, value-added analytics to support risk management, operational efficiency, regulatory compliance, portfolio management, and market research.

The associate must overcome issues of complex data (e.g., VLDB, multi-structured, big data, etc.) to support deployment of advanced techniques (e.g., statistical analysis, linear regression, regularization, machine learning etc.). The associate must also be able to clearly communicate how enterprise information products answer material banking questions to executives and stakeholders.

Qualified candidates must be able to work independently to provide sound economic reasoning, statistical analysis and deliver high quality modeling insights as well as modeling documentation. The ideal candidate is self-directed, collaborative, analytical, and proactive in execution and problem resolution. Specific tasks include:

Develop and design best in class models for valuation of Residential Real Estate.
Develop and design best in class market intelligence applications that support decision making in the residential real estate space.
Attend multiple model governance forums and present model results to an executive level audience.
Develop ROI metrics and recruit funding for specific modeling initiatives related to business needs.
Collaboration with Enterprise Model Risk Management to quickly and efficiently resolve outstanding issues and support documentation as required.
Test new statistical tools and packages

Required/Desired Skills:
5 yrs experience required
Expert knowledge of classical statistical techniques such a linear regression and maximum likelihood regression
Expert knowledge of regularization methods (i.e. Ridge/LASSO/PCA) and machine learning (Classification Trees and Random Forests)
Expertise in data analytics using both SQL and Tableau
Expertise in model development in either R or Python

Desired Skills:

PhD or Master's Degree in engineering, applied mathematics, economics or statistics preferred;