Quantitative Analyst, CoStar Risk Analytics

Quantitative Analyst, CoStar Risk Analytics

<br>

Job Description

<br>

CoStar Group (NASDAQ: CSGP) is a leading global provider of commercial and residential real estate information, analytics, and online marketplaces.  Included in the S&P 500 Index and the NASDAQ 100, CoStar Group is on a mission to digitize the world’s real estate, empowering all people to discover properties, insights and connections that improve their businesses and lives. 

We have been living and breathing the world of real estate information and online marketplaces for over 35 years, giving us the perspective to create truly unique and valuable offerings to our customers.  We’ve continually refined, transformed and perfected our approach to our business, creating a language that has become standard in our industry, for our customers, and even our competitors.  We continue that effort today and are always working to improve and drive innovation.  This is how we deliver for our customers, our employees, and investors.  By equipping the brightest minds with the best resources available, we provide an invaluable edge in real estate. 

CoStar Risk Analytics, located in Boston, MA, works with market participants across the commercial real estate (CRE) lending spectrum. Our solution provides lenders with the ability to better target business, appropriately price loans, make informed approval of credits, actively manage their portfolio with robust stress testing and surveillance measures, assess refinance risk and execute with a disciplined framework. Our clients include both public and private lenders, ranging from commercial banks and financial institutions to pension funds and insurance companies as well as government and rating agencies.

CoStar Risk Analytics is currently looking for a Quantitative Analyst to join the growing Boston based team. This individual will work on the industry leading credit risk model Compass, the calculation engine of CoStar Lender product.

Responsibilities:

• Conduct independent and creative quantitative research applied towards modeling of loan default risk, prepayment risk, recoveries, risk rating, CECL, capital adequacy, and other related credit risk analysis in the lending fields of commercial mortgage, residential mortgage, commercial & industrial, and commercial backed mortgages securities (CMBS).
• Conduct on-going model validation analysis and evaluate the needs for model enhancements and recalibration.
• Translate business needs of analytics, calculation and modeling functionalities into product model features and conduct research on data requirements and selections of model framework.
• Implement data mining techniques to research, select, validate and integrate data for model development and testing.
• Create detailed functional specifications to implement calculations and application of new model features.
• Build prototype model using Python, R or SAS software languages and generate sample test files for model development tests.
• Closely work with product team and IT developers to productize new model features, QA model implementation, identify and fix errors or bugs in the source code of model developments.
• Create comprehensive documentation and presentation decks on model information for both internal and external use.
• Create, update and QA historical and forecast data tables for on-going model implementation.
• Assist with clients’ request on model training, model interpretative analysis, and validation analysis (including sensitivity analysis, impact analysis and attribution analysis) and regulatory model risk management compliance.
• Conduct market research on related model methodology and approaches and regulatory requirements on model adoptions.

Basic Qualifications:

• Bachelor’s degree from an accredited, not-for-profit University or College is required. 
• A track record of commitment to prior employers.
• Master’s degree in Economics/Statistics/Real Estate/Finance/Financial Engineering/Data Analytics, or relevant fields
• 3+ years of relevant work experience in a research-oriented environment and/or in financial services including banking, insurance, investment management, commercial real estate, or accounting
• Knowledge of applied statistical analysis, stochastic processes, econometrics, and quantitative methodologies.
• Excellent programming skills in Python, R, SQL, SAS, Stata, or other languages and the ability to conduct data mining in large dataset
• Accuracy and attention to detail
• Excellent problem-solving skills
• Ability to articulate model features to different audience to provide broad overview or technical details