Senior Vice President, Quantitative Analyst, Structured Finance Analytics

Morningstar

. Provide hands-on technical leadership in structured finance credit rating modelling across RMBS, CMBS, ABS and Structured Credit .

Posted 11/6/2026full-timeFrankfurt • GermanySenior💰 €139,500 - €211,433 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in structured finance credit rating modeling, with a strong foundation in statistical modeling, programming in Python and C++, and the ability to lead complex quantitative projects. Proven capability in mentoring analysts and translating business needs into effective modeling approaches.

Highest-signal resume keywords

Structured Finance Credit ModelingPython ProgrammingQuantitative Credit Risk AnalysisStatistical ModelingProject Coordination

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Structured Finance Default ModelsLoss ModelsFactor ModelsRoll-Rate ModelsSurvival ModelsStatistical ModelingProbability TheoryStochastic CalculusNumerical MethodsData Analysis

Soft Skills

Communication SkillsMentoringOrganizational SkillsIssue ResolutionTeaching Skills

Tools & Technologies

C++NumPyPandasScikit-LearnSciPyAWSLaTeX

Industry Keywords

RMBSCMBSABSCLOsSecuritisation ProductsQuantitative ResearchModel ValidationETL Processes

Tech Stack

Tools & technologies

AWSCloudETLNumpyPandasPythonScikit-LearnC++

About the role

Key responsibilities & impact

Provide hands-on technical leadership in structured finance credit rating modelling across RMBS, CMBS, ABS and Structured Credit
Design, implement and enhance structured finance credit models
Lead complex model and methodology development projects through release
Coordinate technical workstreams, dependencies, timelines and stakeholders across quantitative, ratings, review and Technology teams
Develop, maintain and enhance proprietary Python and C++ modelling libraries
Engage with rating analysts to understand analytical challenges and market trends
Translate business needs into appropriate modelling approaches
Lead quantitative research papers and technical documentation for model and methodology enhancements
Mentor quantitative analysts through technical discussions and practical support
Review models, mathematical derivations, code and technical papers for correctness and numerical robustness
Explain quantitative concepts, model assumptions, results, validation and limitations to less technical colleagues
Develop transparent and interpretable approaches to complex credit risk problems
Research and assess new datasets for model development and credit analysis
Coordinate with Technology to ingest new data sources and develop ETL processes

Requirements

What you’ll need

Minimum of a master’s degree in Mathematics, Engineering, Physics, Economics, Finance, Statistics, or a related quantitative discipline
At least 10 years of relevant professional experience in quantitative credit risk
Substantial hands-on experience developing structured finance default and loss models for RMBS, CMBS, ABS and/or CLOs
Practical experience in factor models, roll-rate models and survival models
Strong programming skills in Python or C++
Experience writing clear research articles and/or technical documentation
Ability to perform rigorous data analysis on large datasets
Deep knowledge of statistical modelling, probability theory, stochastic calculus and numerical methods
Knowledge of numerical integration, Monte Carlo simulation, root-finding and optimisation
Strong modelling judgement and ability to create simple, transparent and interpretable models
Excellent understanding of securitisation products
Experience delivering complex quantitative projects involving multiple teams and interdependent workstreams
Strong organisational, issue resolution and project coordination skills
Demonstrated communication and teaching skills for non-technical audiences
Proven experience mentoring quantitative analysts and reviewing credit models and mathematical derivations
Ability to use AI-assisted coding tools while independently reviewing and validating generated code
Nice to have: experience with a credit rating agency
Nice to have: familiarity with NumPy, Pandas, Scikit-Learn and SciPy for C++ programmers
Nice to have: experience developing cloud applications, preferably AWS
Nice to have: experience using LaTeX

Benefits

Comp & perks

30% annual bonus target
Hybrid work environment with in-person collaboration each week
Flexible work arrangements and other benefits to enhance flexibility
Tools and resources to engage meaningfully with global colleagues