Associate Director of Quantitative Research

The Group: Morningstar's Analytics group turns data into the methodologies, analytics, and insights that help investors make better decisions. We build proprietary intelligence across hundreds of thousands of securities and the portfolios that hold them: equities, fixed income, managed investments, and, increasingly, private and alternative assets. We are applying AI to make that intelligence more accessible and to bring it into investors' research and portfolio workflows. As one of the largest independent sources of fund, equity, and credit data and research in the world, Morningstar is guided by a single mission — empowering investor success.

The Role:

We are seeking an Associate Director of Quantitative Research to lead our Investment & Time Series team. This team owns a broad and growing set of the calculations that underpin Morningstar's data — fund flows, fees, asset allocation, performance, and more. Together, they form a foundational layer that a huge range of downstream products and research depend on getting right.

You will lead a team that operates across two modes: a steady stream of tactical work — client-driven methodology checks, regulatory-driven updates, one-off investigations — and a smaller number of larger initiatives where the team is building deeper, lasting expertise. You will be the team's technical anchor, providing the methodology judgment your team can lean on, and its connective tissue with the many stakeholders — Product, Analytics, Data, and others — whose requests and dependencies flow through this team constantly.

This position is based in our Mumbai office.

Responsibilities:

• Own the team's day-to-day tactical work — client-driven methodology checks, regulatory-driven updates, one-off investigations — ensuring requests are triaged, resolved, and communicated back reliably.
• Operate reliably across multiple concurrent projects, ensuring robust, on-time delivery as request volume grows.
• Lead a smaller number of larger research initiatives each year, building the team's expertise in the areas it's asked to focus on rather than staying purely reactive.
• Serve as the team's methodology anchor: since the team skews less senior, you'll be the one making the calls, reviewing logic, and catching errors before they reach production, not just delegating and checking in.
• Coordinate with Product Management and Data Strategy, Technology, and other stakeholders — including Data Collection, Product teams, and local market experts — translating requests into clear specifications, supporting Product Management's intake-to-production process, and keeping methodologies relevant and well documented.
• Raise the bar on documentation quality and completeness, with the goal of making the team's methodologies easier for others — including client support — to understand and use without needing to ask the team directly.
• Use AI to strengthen how the team documents and validates its work — from AI-assisted specification and documentation to using AI as a check within methodology and QA processes.
• Mentor team members earlier in their careers, building their judgment and technical depth over time, while growing the team's more senior members toward real autonomy in driving projects and owning quality.

Requirements:

• A successful candidate will bring strong, hands-on expertise across a broad range of financial and quantitative domains — such as risk and return analysis, security- and portfolio-level analytics, time series methods, and asset allocation — with enough depth to make sound, independent methodology judgments across all of them.
• CFA designation is considered a strong asset.
• Highly proficient with modern data tools — AWS, datalake, Python notebooks — with the coding depth to work efficiently and independently across large, varied datasets.
• Track record of using AI thoughtfully in a technical or analytical role — not just adopting a tool, but changing how documentation or quality checks actually get done.
• Demonstrated ability to manage and prioritize a high volume of concurrent, often time-sensitive requests without sacrificing quality.
• A track record of growing team members at different career stages — from building judgment in early-career staff to giving senior staff enough ownership that they no longer need you for every decision.
• Experience earning trust with non-technical stakeholders — able to point to a time a technical or methodology explanation actually changed how a stakeholder made a decision, not just kept them informed.
• Minimum 7 years of experience in quantitative research, financial data, or a related analytical field.

Morningstar is an equal opportunity employer.

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.