About the Team

The Structured Finance Analytics Team is composed of a Quant team, a Data Analytics team, a Solutions team, and a Cashflow Modelling team. The Quant team has been growing over the last few years, comprising a global team of a dozen people today, located in the US and in Europe. The Quant team builds models and analytical tools to help rating analysts assess the credit risk of a transaction.

The Role

As a Quant Analyst you will execute proprietary research for building various types of credit rating models, such as factor models and predictive models covering asset classes of ABS, CMBS, RMBS and Structured Credit. The Quant team will collaborate with members from the Credit Ratings, Credit Practices, Methodology Review Function, Data Engendering and Technology teams to create class leading models that are as innovative as understandable in the marketplace.

Responsibilities

• Support rating methodology development and participate in the implementation of quantitative models such as credit predictive models.
• Develop, maintain and enhance proprietary Python and C++ libraries related to model building.
• Leverage structured and unstructured datasets to build new quantitative frameworks to assist analysts in informed decision making.
• Assisting development of analytics-based solutions, taking ownership of the design and development of solutions to scale out information ingestion, storage, computation (training/inference), validation.
• Participate in analyst conversations to understand ongoing analyst issues and merging market trend.
• Contribute to the development and writing of quantitative research papers supporting model development, methodology enhancements, and analytical innovation.

Requirements

• Bachelor’s degree; Master’s degree or PhD preferred in Mathematics, Engineering, Physics, Economics, Finance, Statistics, or a related quantitative discipline.
• Minimum 5 years of experience within a rating agency (preferred) or financial institution.
• Minimum 5 years of hands-on experience in RMBS/ABS/CLO defaults and losses modelling.
• Coding skills in a major programming language Python or C++ and experience writing research articles and/or technical documentation using LaTeX.
• Strong knowledge of statistical modelling, probability theory, numerical analysis and stochastic calculus.
• Strong knowledge of numerical methods (numerical integration, Monte Carlo simulation, root-finding and general optimisation techniques).
• Excellent understanding of securitisation products.
• Understanding both business and technical requirements, and the ability to serve as a conduit between technical and non-technical departments.

Nice to have

• CQF or postgraduate degree in quantitative finance, economics, or STEM fields is highly desired.
• Exposure to main Python packages for numerical computing and Machine Learning / Data Science (NumPy, Pandas, Scikit-Learn and SciPy).
• Ability to perform rigorous data analysis on large datasets.
• Experience developing cloud applications (AWS preferably).

If you receive and accept an offer from us, we require that personal and any related investments be disclosed confidentially to our Compliance team. These investments will be reviewed to ensure they meet Code of Ethics requirements. If any conflicts of interest are identified, then you will be required to liquidate those holdings immediately. In addition, dependent on your department and location of work certain employee accounts must be held with an approved broker (for example all, U.S. employee accounts). If this applies and your account(s) are not with an approved broker, you will be required to move your holdings to an approved broker.

Base Salary Compensation Range

Bonus Target:

We expect the compensation and target bonus for this role to fall within the stated range. The specific compensation offered will depend on the candidate’s qualifications, experience, and other job-related factors.

Morningstar's hybrid work environment gives you the opportunity to collaborate in-person each week as we've found that we're at our best when we're purposely together on a regular basis. In most of our locations, our hybrid work model is four days in-office each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you'll have tools and resources to engage meaningfully with your global colleagues.