As a Technical Manager, you will oversee an engineering team dedicated to the implementation and maintenance of statistical software applications, research databases, and other data products. This is a highly visible role with significant interaction with both upstream and downstream stakeholders across Technology, Data, Products, Sales/Service, and Research.
The Manager will take ownership over approved research products transitioning them 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. Of course, quantitative research methodologies usually have significant technology demands and data requirements. Therefore, the Manager will be expected to participate in methodology development as it relates to considering tradeoffs between methodological rigor and technological feasibility.
You will be expected to keep pace with trends in software engineering, computer science, data science, and financial research industries. You will be expected to autonomously contribute to a comprehensive technology strategy for the Quant Research group that results in greater products, more robust processes, better communication, and faster delivery times.
Our products are global, so you need to think globally.
As a manager and mentor, you will be expected to adopt an "iron sharpens iron" attitude where the focus is on making everyone better. Not focusing on your colleagues’ shortcomings but recognizing their strengths. Respect is foundational. We want to pull people up and not push them down.
This position reports to the Senior Manager of Quantitative Research, Technology.
- Technical SME – Act as a Technical SME for Quant Engineering suggesting best practices and aligning to best practices of Software Engineering.
- People Management – you will manage a team of software engineers, Data Engineers, DevOps Engineers, QA Engineers. Mentoring the people below you by developing their skills and positioning them for success is critical.
- Build Statistical Applications and Data Products – own the implementation and maintenance statistical software applications, research databases, and other data products from prototype phase to fully-fledged, scalable, client-facing service.
- Project Management – utilize and advocate for Agile project management practices to track projects, coordinate with internal groups, and resolve conflicts.
- Client Interaction – you will be responsible for onboarding clients to our products and services. Willingness to participate in periodic calls during US or European hours is preferred. Moreover, you will be expected to assist in the resolution of client issues and questions via email.
- Minimum of 8-10 years of hands on experience in software engineering.
- Excellent people management skills.
- An advanced degree in engineering, computer science, statistics or related field.
- Good knowledge of Python, Object Oriented Programming & Cloud (AWS certification preferred).
- Experience deploying Analytics/Machine Learning solutions using services in the Amazon AWS ecosystem (Lambda, EC2, RDS, EMR).
- Knowledge of statistical/ML/AI terminologies as Clustering, Regression, Classification, NLP, Word Embeddings.
- Preferably having experience in Financial Domain managing both projects and operations.
- Expertise with DevOps tools (e.g. Splunk, Git, uDeploy, Jenkins, Control-M).
- Experience with Agile software engineering practices.
- Experience managing large teams building Data Analytics solutions using R & Python.
- Experience with UNIX/Linux including basic commands and shell scripting.
- Familiarity with mutual fund, fixed income, and equity data is a plus.
- Intellectual curiosity for the world of Finance & quantitative research.
- Fluent in both oral and written English.
Morningstar is an equal opportunity employer.
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