Your Team ResponsibilitiesThe Emerging Risks R&D team is building MSCI's next-generation capability for measuring and classifying portfolio exposure to economy-wide structural themes.
Our team develops AI-native data pipelines and production models that enable investors to quantify company and portfolio exposure to emerging risks like AI disruption, climate change, geopolitical risk, supply chain disruption, and demographic shifts.
You'll build production-level models and pipelines that deliver transparent, financially material signals for portfolio allocation decisions, working closely with AI and Engineering teams to translate research prototypes into scalable, operational systems.
Your Key Responsibilities- The Emerging Risks R&D team is building MSCI's next-generation capability for measuring and classifying portfolio exposure to economy-wide structural themes.
- Our team develops AI-native data pipelines and production models that enable investors to quantify company and portfolio exposure to emerging risks like AI disruption, climate change, geopolitical risk, supply chain disruption, and demographic shifts.
- You'll build production-level models and pipelines that deliver transparent, financially material signals for portfolio allocation decisions, working closely with AI and Engineering teams to translate research prototypes into scalable, operational systems.
Your skills and experience that will help you excel- Bachelor's or Master's degree in Computer Science, Economics, Finance, or another quantitative field with demonstrated AI/ML expertise
- 1-3 years of hands-on experience applying large language models and NLP techniques to text analysis, document classification, or information extraction
- Strong Python programming skills with experience in modern AI frameworks (LangChain, LangGraph, LlamaIndex, HuggingFace) and data processing libraries
- Experience translating ambiguous, qualitative concepts into taxonomies, classification schemas or decision frameworks suitable for systematic model design
- Demonstrated ability to design prompts and decision rules that operationalize conceptual frameworks in LLM-based pipelines
- Comfortable working in a fast-paced research environment with evolving priorities and iterative prototyping
Preferred Qualifications- Familiarity with vector databases, embedding models, and semantic search architectures
- Experience with applied AI research methodologies and agentic workflows for information extraction, synthesis and decision support
- Experience with cloud data platforms (Snowflake, Azure) and production ML deployment
- Familiarity with knowledge graphs and supply chain network analysis
