Descripción de la oferta
This job is with Morningstar, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ+ business community. 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.
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.
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.
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.
Contribute to the development and writing of quantitative research papers supporting model development, methodology enhancements, and analytical innovation.
Bachelor's degree; Master's degree or PhD preferred in Mathematics, Engineering, Physics, Economics, Finance, Statistics, or a related quantitative discipline.
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).
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. 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. No matter where you are, you'll have tools and resources to engage meaningfully with your global colleagues.
