Director/VP, AI Research — Private Assets RD

Saved job description

MSCI's Private Assets R&D group develops the methodologies, models, and indexes underpinning MSCI's offerings across private equity, private credit, and private real assets. Drawing on data assets from the Burgiss, IPD, and RCA acquisitions, the team serves the world's leading institutional investors — pension funds, sovereign wealth funds, endowments, and asset managers.

You will join a senior research team at the forefront of bringing institutional-grade analytics to private markets. As Executive Director, AI Research, you will report directly to the Managing Director, Head of Private Assets R&D, and be responsible for the build-out of AI-native research capabilities across the group. You will collaborate closely with researchers, product heads across private equity, private credit, and real assets, and MSCI's technology and data engineering teams to translate AI research advances into scalable client solutions.

Define and drive the AI research agenda for Private Assets R&D, identifying where AI creates durable competitive advantage in private markets analytics

Develop AI-driven approaches to evaluated pricing and comparable selection, leveraging unstructured text, financial filings, and embedding-based similarity models

Extend MSCI's nowcasting and cash flow forecasting capabilities using modern ML methods — including LLM-based models — with a focus on explainability for institutional clients

Design and deploy agentic AI workflows and prototypes, accelerating the path from research concept to client-facing product

Automate high-value recurring research tasks including market commentary generation and model parameter re-estimation

Represent Private Assets R&D in client engagements and through published research

Degree in Computer Science, Computational Science, Statistics, Mathematics, or a related quantitative field

5+ years of experience in applied AI/ML research or quantitative research, with a track record of building and deploying AI/ML powered tools or applications in production

Deep expertise in Python and modern ML frameworks (plus, ideally: R); hands-on experience with large language models, retrieval-augmented generation, and/or deep learning

Proven ability to own a research agenda end-to-end — from hypothesis through model development, validation, and client-ready delivery

Strong communication skills — able to present complex methodological concepts credibly to senior stakeholders and institutional clients

Preferred: domain knowledge in private markets, asset management, or institutional investment analytics

Preferred: experience with unstructured data (NLP, document parsing, web extraction) applied to financial problems

Preferred: familiarity with agentic AI frameworks and rapid prototyping tools