SVP, Structured Finance Quant Modeling | Hybrid Leader

Morningstar, Inc.; United States; Remote; USD 156,000 - 236,000; Full time

Job summary

Morningstar, Inc. seeks a Senior Vice President in the Quant team to provide hands-on technical leadership for structured finance credit rating modelling across RMBS, CMBS, ABS and structured credit.

You will collaborate with Ratings, Practices, Methodology Review, Data Engineering and Technology to deliver rigorously tested models. You will mentor analysts, lead complex projects from design to release, and develop transparent, interpretable modelling approaches while using AI-assisted coding

Qualifications

• Master’s degree in Mathematics, Engineering, Physics, Economics, Finance, Statistics, or related quantitative field.
• PhD preferred.
• At least 10 years in quantitative credit risk with hands-on modelling experience for RMBS/CMBS/ABS/CLOs.
• Proficient in Python or C++ with ability to write clear research papers or technical docs.
• Rigorous data analysis on large datasets; strong mathematical judgement and numerical methods.

Responsibilities

• Own design, implementation and enhancement of structured finance credit models.
• Lead complex model and methodology projects with cross-team coordination.
• Develop and maintain proprietary Python and C++ modelling libraries.
• Engage with rating analysts to translate business needs into modelling approaches.
• Mentor analysts and review models, code, and derivations for accuracy.

Skills

Python

C++

Quantitative modelling

Data analysis

Education

Master's degree

PhD preferred

Tools

NumPy

Pandas

SciPy

LaTeX

Job description

Morningstar, Inc. seeks a Senior Vice President in the Quant team to provide hands-on technical leadership for structured finance credit rating modelling across RMBS, CMBS, ABS and structured credit.

You will collaborate with Ratings, Practices, Methodology Review, Data Engineering and Technology to deliver rigorously tested models. You will mentor analysts, lead complex projects from design to release, and develop transparent, interpretable modelling approaches while using AI-assisted coding