The Senior Principal Quantitative Analyst plays a critical role in ensuring the accuracy, integrity, and reliability of quantitative data that powers Morningstars financial models, analytics, and decision-making processes. This role is a cornerstone of the Managed Investment Data (MID) program, which collects, standardizes, and enriches global fund data supporting investors, advisors, and institutions through trusted data and insight. The analyst will lead quantitative data quality design and implementation, develop AI/ML-based validation frameworks, and collaborate with cross-functional teams to strengthen data governance and model readiness. This role reports to the Director, Quality & Transformation within the Managed Investment Data team based in Mumbai.
Job Responsibilities :
– Lead the design, implementation, and enhancement of quantitative data quality frameworks, encompassing statistical validation and anomaly detection.
– Develop AI/ML-driven predictive quality checks, enabling proactive data error prevention and model trustworthiness.
– Apply advanced statistical methodologies linear/non-linear modeling, time series analysis, and Bayesian inference to detect quality drifts and signal inconsistencies.
– Collaborate with quantitative researchers, data scientists, and engineers to ensure data readiness for quantitative models and investment algorithms.
– Create automated, scalable, and auditable data validation pipelines, supporting real-time data monitoring and exception reporting.
– Partner with stakeholders to uphold data governance, privacy, and regulatory compliance standards (MiFID, ESMA, SEC).
– Mentor and guide junior analysts, fostering a culture of excellence, continuous learning, and innovation in quantitative analysis.
– Communicate complex data quality insights and statistical findings in simple terms to senior leadership and non-technical stakeholders.
– Drive innovation through automation, reproducible modeling pipelines, and deployment of ML-based data correction systems.
– Contribute to the modernization of Morningstars data architecture by integrating data observability, telemetry, and metadata-driven quality measures.
Requirements :
– Strong foundation in quantitative finance, econometrics, and applied statistics.
– Deep understanding of financial instruments, fund structures, and performance modeling.
– Proven ability to work with large-scale, structured and unstructured data.
– Excellent analytical, problem-solving, and statistical reasoning skills.
– Strong stakeholder management, communication, and presentation skills.
– Ability to work in a cross-functional, fast-paced environment, and lead through influence.
Desired Candidate Profile :
– Masters degree in Data Science, Statistics, Mathematics, Financial Engineering, or Quantitative Finance.
– Professional certifications such as CFA, FRM, CQF, or Six Sigma Black Belt preferred.
– 10+ years of experience in quantitative analytics, model validation, or data quality engineering within financial services, asset management, or fintech.
– Expertise in Python, R, SQL, and familiarity with tools such as MATLAB, SAS, or TensorFlow.
– Experience in AWS ecosystem (S3, RDS, Glue, Athena) and modern data quality platforms.
– Hands-on experience with AI/ML frameworks (scikit-learn, PyTorch, TensorFlow) for anomaly detection and predictive data correction.
– Familiarity with data governance and regulatory standards (GDPR, SEC, ESMA, MiFID).
– Proficiency in Lean, Agile, and automation-first approaches for process improvement.
– Entrepreneurial mindset with a passion for innovation and scalability.
– Strong leadership, mentorship, and collaboration abilities.
– Flexible to adapt to evolving data and technology landscapes.
Key Competencies :
– Statistical Expertise : Deep proficiency in hypothesis testing, regression modeling, and time-series forecasting.
– AI/ML Integration : Building and deploying predictive quality and anomaly detection models.
– Automation Mindset : Experience with data pipelines, ETL automation, and observability frameworks.
– Data Governance : Comprehensive understanding of metadata management, lineage, and auditability.
– Business Acumen : Translating technical insights into actionable business intelligence.
– Leadership : Guiding teams through analytical rigor, innovation, and continuous improvement.
