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 Research Engineer, you will be a leading contributor in the implementation and maintenance of statistical software applications, research databases, and other data products. This role requires significant interaction with both upstream and downstream stakeholders across Technology, Data, Products, Sales/Service, and Research.
The Research Engineer will assist in the transition of approved research products from a prototype phase to a fully-fledged, scalable, and client-facing service. Often, these services must be integrated into Morningstar's platform of financial products, such as Direct, so that our clients can use these software tools in the investment decision-making process.
We are looking for an individual who possesses strong technical development skills, an ability to follow analyst requirements and technical specifications for robust code, and a passion for investment research.
This position reports to the Tech Manager of the Quantitative Research team.
Responsibilities:
- Create, upgrade, and maintain databases and production applications that support quantitative research.
- Develop and deploy production applications that incorporate numerical techniques such as linear algebra, machine learning, statistics, and optimization.
- Maintain a robust development environment via strong documentation and version control.
- Find creative solutions to complex development problems using all technologies at your disposal, especially big data and cloud technologies.
- Build interactive tools and visualizations for quantitative methodologies.
Requirements:
- Minimum of 1-2 years of experience in software engineering
- An advanced degree in engineering, computer science, statistics or related field is preferred
- Experience with either Python or C#/.NET is essential, while experience with both is desirable; some C/C++ skills are highly desirable. Experience with Python packages like pandas, scikit-learn, tensorflow, numpy is a plus.
- Familiarity with DevOps tools (e.g. Splunk, Git, uDeploy, Jenkins, Control-M)
- Familiarity with Agile software engineering practices
- Familiarity with back-end XML, relational, and file-based databases (e.g. SQL, Postgres, Redshift, Netezza, HDFS)
- Experience with UNIX/Linux including basic commands and shell scripting
- Experience developing and deploying solutions using services in the Amazon AWS ecosystem (Lambda, EC2, RDS, EMR) is a plus
- Experience with the Hadoop stack (MapReduce, Pig, Hive, Nifi, Spark) is a plus
- Experience with at least one statistical modeling language (e.g. R, MATLAB, Python) is a plus
- Experience designing, developing, and implementing ETL is a plus
- Experience developing APIs and microservices hosted in the cloud
- Intermediate knowledge of statistical methods is a plus
- Familiarity with common data cleaning and munging techniques is a plus
- Familiarity with automation tools such as Puppet or Chef is a plus.
- Familiarity with statistical software, linear algebra, optimization, and information visualization is a plus.
- Familiarity with mutual fund, fixed income, and equity data is a plus
- Progress towards a CFA is a plus
- Intellectual curiosity for the world of quantitative research
- Fluent in both oral and written English.
Morningstar is an equal opportunity employer.
Morningstar's hybrid work environment gives you the opportunity to work remotely and collaborate in-person each week. We've found that we're at our best when we're purposely together on a regular basis, at least three days 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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