Moody's Analytics

Position Title/Short Description

Full Text of JOE Listing:

The Role:

Moody’s Analytics Commercial Real Estate (CRE) business unit champions the mission to provide holistic solutions through insightful analytics and integrated workflows informed by enriched data. As a quantitative researcher/modeler in the Analytics team, you will work with a team of expert researchers and data scientists to conduct quantitative research and develop statistical models. The role will also involve partnering with product management and other teams to implement the analytics solutions and to integrate rich CRE data with powerful predictive models.

Responsibilities:

We operate in a highly dynamic and innovative environment, and we adapt our roles and responsibilities to address constantly evolving business needs with the goal to solidify and expand Moody’s world-class CRE analytics solutions.

Sample responsibilities include:

• Applying urban economic theory and spatial modeling technique to analyze geographic/demographic and commercial real estate data;
• Estimating statistical relationships between locational attributes and spatial patterns in various metropolitan areas with econometric and machine learning techniques;
• Conducting complex theoretical and empirical research on measurements of investment/credit risk in commercial real estate and other related financial assets;
• Developing commercial mortgage loan-level probability of default and loss severity models;
• Forecasting commercial real estate market trends based on economic conditions and local demand and supply;
• Assessing and predicting the financial impact of climate change on commercial real estate properties;
• Providing research expertise to develop prototypes and support product and software development activities;
• Present research findings to technical and non-technical audiences.

Qualifications:

• Ph.D. in economics, finance, real estate, regional studies, geography, urban planning, or a closely related field;
• Expert knowledge in economic and/or financial modeling;
• Extensive experience with large datasets, particularly those related to real estate, demographics, and geographic information;
• Proficiency in statistical analysis, machine learning, and programming tools such as Python, R, SAS, or Matlab;
• Knowledge in spatial analysis and GIS tools such as ArcGIS or QGIS is a plus;
• Excellent written and oral communication skills.