Role: Quantitative Analyst, Quantitative Research
The Group: Morningstar’s Quantitative Research Group creates independent investment research and data-driven analytics designed to help investors and Morningstar achieve better outcomes by making better decisions. We utilize statistical rigor and large data sets to inform the methodologies we develop. Our research encompasses hundreds of thousands of securities within a large breadth of asset classes including equities, fixed income, structured credit, and funds. Morningstar is one of the largest independent sources of fund, equity, and credit data and research in the world, and our advocacy for investors’ interests is the foundation of our company.
The Role: As a Senior Quant analyst, you will work in a team dedicated to researching data intensive products for investment management industry. Most of the research is integrated to Morningstar’s core products (Direct, Advisor Workstation etc.) and teams such as Morningstar Investment Management, Morningstar Indexes, Morning Credit Ratings, Pitchbook, Sustainalytics etc.
The ideal candidate will blend financial knowledge, investment and portfolio construction expertise, Quant Modeling skills and operational know how. This position reports to the Manager of Quantitative Research, Technology.
- Support methodology development, Quant Model builds & enhancements for core Quant products as Risk Model, Asset Flows Forecast, Quant Ratings, Portfolio Construction, etc.
- Drive independent research, publish research papers in asset allocation analysis, portfolio optimization, risk model, ESG, fund flows etc. using principles of modern portfolio theory, statistics.
- Leverage new structured and unstructured datasets to build new Quant frameworks that would help investors in informed decision making.
- Participate in client conversations for understanding ongoing investor issues, alongside increasing reach of Morningstar Quant offerings.
- 1 to 6 years of investment/quant research experience with emphasis on quant finance, mutual fund analysis, asset allocation, and/or portfolio construction.
- CFA, FRM, CQF or postgraduate degree in finance, economics, mathematics, statistics is preferable.
- Good experience in developing Finance/ Statistics based applications, using proven technologies such as R, Python, PySpark using Jupyter Notebooks.
- Understanding of both business and technical requirements, and the ability to serve as a conduit between product, research, technology, and external clients.
- Knowledge of statistical models (e.g. Regression, forecasting, optimization, Monte Carlo simulations, etc.).
- Experience developing Financial Engineering/ Statistical applications on cloud (AWS)
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