Lead Quantitative Analyst
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 Lead Quantitative Analyst, you will be Morningstar’s thought leader in the managed products space. In addition, you will be responsible for creating original investment research that helps investors. You will work as part of a large team dedicated to building out a cutting-edge suite of risk and ratings solutions. You will assist in the implementation and maintenance of statistical software applications, research databases, and other data products. A successful candidate will have impeccable interpersonal and communications skills, a solid quantitative mindset, and a strong understanding of finance. This position is based in our Chicago office.
Responsibilities:
Faithfully represent the work of the quantitative research team to internal stakeholders and external clients
Assist in the development of production applications that incorporate numerical techniques such as linear algebra, machine learning, statistics, and optimization.
Be a subject-matter expert on managed products and the overall industry
Generate original investment research focusing on managed products with the goal of improving outcomes for investors
Requirements:
A bachelor’s degree in quantitative or financial discipline (Masters or higher is desirable)
Minimum 5 years of experience in the financial industry
Excellent written and oral communication skills
Meet strict deadlines and juggle various tasks while maintaining excellent attention to detail and impeccable accuracy in running and proofing analysis
Extensive knowledge of statistical methods
Ability to work collaboratively in a team environment, while also taking ownership of projects independently with minimal supervision
Expertise in statistical modeling languages (R or Python)
Experience with Python packages like pandas, scikit-learn, numpy is a plus.
Familiarity with Agile engineering practices
Familiarity with SQL
Familiarity with common data cleaning and munging techniques
Familiarity with linear algebra, optimization, and information visualization is a plus