Analyst, Quant, Structured Finance Analytics

About the role:

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 from two to eight people today. The Quant team builds models and analytical tools to help rating analysts to assess the credit risk of a transaction. Although some projects are global, this team mostly covers European needs.

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, Covered Bond, RMBS and Structured Credit.

The Structured Finance Ratings Modeling team will collaborate with members from the Credit Ratings, Credit Practices, Independent Review, Data and Technology teams to create class leading models that are as innovative as they are easy to understand in the marketplace.

You will be expected to adopt an "iron sharpens iron" attitude where the focus is on making everyone better. The ideal candidate will demonstrate quantitative skills in statistics, machine learning, numerical methods and software engineering. This position reports to the Associate Managing Director who leads the team.

Responsibilities:

Support rating methodology development and participate in the implementation of quantitative models such as credit predictive models.

Maintain and enhance proprietary Python and R libraries related to model building.

Leverage structured and unstructured datasets to build new Quant 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 information ingestion, storage, computation (training/inference), validation.

Participate in analyst conversations for understanding ongoing analyst issues.

Requirements:

Master’s degree in mathematics, engineering or physics.

Up to 2 years of investment research / rating agencies experience with emphasis on fixed income research / analysis, credit modelling.

Coding skills in a major programming language such as Python, C/C++ or R / MATLAB.

Knowledge of probability theory, numerical analysis and stochastic calculus.

Knowledge of numerical methods (numerical integration, Monte Carlo simulation, root-finding and general optimisation techniques).

Nice to have:

CQF certification.

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 applications on cloud (AWS preferably).

Understanding of both business and technical requirements, and the ability to serve as a conduit between technical and non-technical departments.

Familiarity fixed income and structured finance.

About Us

Morningstar DBRS is a leading provider of independent rating services and opinions for corporate and sovereign entities, financial institutions, and project and structured finance instruments globally. Rating more than 4,000 issuers and 60,000 securities, it is one of the top four credit rating agencies in the world.

Morningstar DBRS empowers investor success by bringing more transparency and a much-needed diversity of opinion in the credit rating industry. Our approach and size allow us to be nimble enough to respond to customers' needs in their local markets, but large enough to provide the necessary expertise and resources they require. Market innovators choose to work with us because of our agility, tech-forward approach, and exceptional customer service.

Morningstar DBRS is the next generation of credit ratings.

If you receive and accept an offer from us, we require that personal and any related investments be disclosed confidentiality to our Compliance team (days vary by region). 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.

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.

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