Quantitative Credit Risk Associate

Coda Search│Staffing; New York (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

Coda Search is partnering with a globally recognized alternative asset manager in NYC to hire a Quantitative Credit Risk Associate. You will own, develop, and maintain PD models and related loss estimation frameworks across the credit platform.

This build‑oriented role requires close collaboration with deal and portfolio teams to deliver robust risk measures. Requirements include 2+ years in quantitative credit risk, a STEM degree, and proficiency in Python and SQL.

Qualifications

• 2+ years in quantitative credit risk or related role in finance.
• Bachelor's or Master's degree in a quantitative discipline (STEM).
• Strong applied statistical modeling and PD modelling experience.

Responsibilities

• Build, calibrate, and maintain PD models for private middle‑market borrowers.
• Develop LGD and EAD models reflecting collateral, liens, covenants, and recoveries.
• Extend framework across asset classes to pool-level and cash flow modeling.
• Map internal risk ratings to agency‑equivalent scales and external studies.
• Back‑test model output against defaults, downgrades, and recoveries.

Skills

PD modelling

Python

SQL

Credit risk

Model validation

Communication

Education

STEM degree

Tools

Python

SQL

Job description

Coda Search is partnering with a globally recognized alternative asset manager that's hiring a Quantitative Credit Risk Associate for its credit platform in NYC. The Quantitative Credit Risk Associate will own, develop, and maintain the probability of default modeling framework and related loss estimation models across the credit platform. This is a build-oriented seat and will partner closely with the deal and portfolio teams.

Responsibilities

• Build, calibrate, and maintain PD models for private middle‑market borrowers
• Develop companion LGD and EAD models reflecting collateral packages, lien position, covenant structures, and observed workout recoveries.
• Extend the framework across asset classes where borrower‑level modeling does not apply i.e pool‑level and cash flow modeling for asset‑based and structured exposures.
• Map internal risk ratings to agency‑equivalent scales and external default studies.
• Back‑test model output against realized defaults, downgrades, and recoveries

Qualifications

• 2+ years of experience in quantitative credit risk, credit modeling, model validation, or a closely related quantitative role at an asset manager, bank, rating agency, insurer, or specialty finance firm.
• Bachelor's or Master's degree in a quantitative discipline (STEM)
• Strong applied statistical modeling and PD modelling experience required
• Proficiency in Python and SQL. Comfort working
• Working fluency in credit fundamentals: capital structures, leveraged finance mechanics, credit agreement terms, and financial statement analysis.
• Ability to communicate model output and its limitations clearly to non-quantitative investment professionals