Job Description:
The Principal Quantitative Quality Analyst is a senior leader in ensuring the observability, reliability, and quality of data used across Morningstar's advanced quantitative models and analytics platforms. This role involves setting strategic direction for quality observability frameworks, employing cutting-edge statistical and AI/ML techniques, and driving innovation in data governance. As a key expert in quantitative data quality, the candidate will collaborate with internal and external stakeholders to establish and maintain the highest standards of data accuracy and usability for managed investment data. This role also serves as a mentor and thought leader, influencing how data quality impacts decision-making across the organization.
Key Responsibilities:
- Define and implement a comprehensive strategy for quantitative data quality observability, including monitoring frameworks and metrics.
- Develop advanced statistical models and AI/ML-driven systems to proactively detect and resolve data anomalies, ensuring data integrity and reliability with 15years of experience
- Lead initiatives to enhance observability tools, enabling real-time insights into data flows, transformations, and potential quality issues.
- Collaborate with cross-functional teams, including data scientists, engineers, and business leaders, to integrate observability practices into data workflows.
- Establish best practices for data governance, compliance, and standardization to meet regulatory and business requirements.
* Conduct deep-dive analyses on data pipelines and systems to identify vulnerabilities and recommend innovative solutions for long-term improvement. - Represent Morningstar in external forums and partnerships to drive industry standards in quantitative data quality and observability.
- Mentor junior analysts and team members, sharing expertise in statistical methods, data observability, and quality frameworks.
* Communicate key findings, trends, and recommendations to senior leadership, enabling data-driven strategic decisions.
Required Competencies:
- Observability Expertise: Advanced knowledge of monitoring and observability tools for quantitative data pipelines, including anomaly detection and trend analysis.
- Statistical & Computational Skills: Mastery in statistical modelling, including linear/non-linear models, Bayesian analysis, and hypothesis testing.
- AI/ML Leadership: Proven ability to design and implement AI/ML-driven solutions for real-time quality monitoring and anomaly detection.
- Strategic Vision: Ability to define and execute a data quality observability strategy aligned with organizational goals.
- Collaboration: Strong experience in working across data science, engineering, and business teams to foster a culture of proactive data quality management.
- Data Governance Expertise: Comprehensive understanding of regulatory compliance, governance frameworks, and data management best practices.
- Programming & Automation: Expertise in programming languages (Python, R, SQL) and proficiency with automation tools for data validation and monitoring.
- Leadership & Influence: Exceptional mentoring skills and the ability to lead cross-functional teams, influencing stakeholders at all levels.
- Problem-Solving: Advanced analytical capabilities to identify root causes of complex data quality issues and implement forward-thinking solutions.
Morningstar is an equal opportunity employer.
402_MstarResSoAfr Morningstar Research Pty Ltd (South Africa) Legal Entity
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.
