Job Description

Position:

Senior Principal Quantitative Analyst

Department:

Managed Investment Data (MID) - Quality & Transformation

No of Positions:

1

Shift:

General Shift

The

Senior Principal Quantitative Analyst

plays a critical role in ensuring the accuracy, integrity, and reliability of quantitative data that powers Morningstar's 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 Morningstar's 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

Master's degree in

Statistics, Mathematics, Financial Engineering, Data Science, 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.

Morningstar is an equal opportunity employer.

We celebrate diversity and are committed to creating an inclusive environment for all employees.


Morningstar's hybrid work environment gives you the opportunity to collaborate in-person each week as we've found that we're at our best when we're purposely together on a regular basis. In most of our locations, our hybrid work model is four days in-office 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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