Quantitative Modeler - Associate Director (NY)
Position Title: Quantitative Modeler - Associate Director (NY)
Entity: KBRA Holdings LLC
Employment Type: Full-time
Location: New York
Summary/Overview:
KBRA (Kroll Bond Rating Agency, LLC) is seeking a Quantitative Modeler with experience in building and maintaining credit risk models. The role will support credit analysis across several credit rating sectors and is located in our New York office. In this role, you will develop, implement and monitor quantitative models to support bond rating methodologies, risk assessment, and market analytics. You will collaborate with cross-functional teams to ensure that our models are robust, transparent, and compliant with regulatory standards.
About the Job:
- Develop, and implement advanced quantitative models for credit risk assessment, including statistical models, portfolio credit default models (e.g., Merton Model, Monte Carlo), and fixed income analysis.
- Assist in the development and publication of rating methodologies, individual transaction reports, and special topic and market commentaries.
- Provide analytic and technical support in the areas of scenario design, forecasting, governance and control.
- Monitor, test, and document existing quantitative models of credit risk assessment and rating models, including loan pool analysis, modeling and analysis of bond structure and cash flows.
- Mentor and lead junior members of the Quantitative Modeling team, fostering a culture of collaboration, skill development, and knowledge sharing.
- Collaborate with Model Risk Management to ensure compliance with internal policies, procedures, and regulatory requirements.
- Present complex quantitative concepts to both technical and non-technical audiences in a clear and concise manner.
- Document modeling choices, methodologies, and performance assessments clearly for audit and regulatory purposes.
You will be successful in this role if you have:
- Advanced degree (Master’s or Ph.D.) in a technical or quantitative discipline such as Statistics, Mathematics, Physics, Electrical Engineering, Economics, or Computer Science.
- Five (5) or more years of experience in applied data analysis, quantitative modeling, and model testing and validation in an industry setting such as a rating agency or regulated bank, preferably in the financial sector.
- Strong background in credit risk modeling, fixed income securities, and financial markets.
- Expertise in building portfolio credit default models (e.g., Merton Model, Monte Carlo Model) and estimating their parameters.
- Familiarity with statistical methods, their appropriate application, and the risks of misuse.
- Proficiency in programming languages such as Python, R, and SQL, with knowledge of data science collaboration strategies, including Git and agile development practices.
- Strong oral and written communication skills, with the ability to present complex quantitative concepts to technical and non-technical audiences.
- Proven ability to document modeling choices and performance assessments for audit and regulatory purposes.
- Demonstrated ability to collaborate effectively with teams, take initiative, and work independently.
Salary Range:
The anticipated annual base salary range for this full-time position is $130,000 to $180,000. Offer amounts are determined by factors such as experience, skills, geography, and other job-related factors.
Benefits:
- A hybrid work schedule (Tuesdays, Wednesdays, Thursdays in the office)
- Competitive benefits and paid time off
- Paid family and disability leave
- 401(k) plan, including employer match (100% vested)
- Educational and professional development financial assistance
- Employee referral bonus program
- Cell phone provided
About Us:
KBRA is a full-service credit rating agency registered in the U.S., the EU and the UK, and is designated to provide structured finance ratings in Canada. KBRA’s ratings can be used by investors for regulatory capital purposes in multiple jurisdictions.
More Information:
KBRA encourages applications from all qualified individuals without regard to race, color, religion, gender, sexual orientation, gender identity or expression, age, national origin, marital status, citizenship, disability, and veteran status or any other basis prohibited by federal, state or local law.
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