Responsibilities

Design, develop, and maintain data science and AI-driven applications supporting multiple MSCI business lines.
Implement machine learning, NLP, and GenAI solutions from experimentation through production.
Build and integrate LLM-powered capabilities including RAG, summarization, classification, extraction, and reasoning.
Develop agentic AI solutions using LLMs, LangChain, LangGraph, and related frameworks.
Apply prompt engineering, orchestration, evaluation, monitoring, text processing, embeddings, and vector-based retrieval practices.
Integrate AI services with MSCI data science platforms and APIs and collaborate with Data, Technology, Engineering, Product Management, Quality Assurance, Data Operations, and IT Infrastructure teams.
Ensure AI systems meet standards for scalability, performance, reliability, maintainability, testing, code quality, and production readiness.
Contribute to CI/CD pipelines and DevOps processes for continuous integration, deployment, and monitoring.
Implement AI governance controls supporting transparency, traceability, auditability, reproducibility, security, responsible AI, and regulatory compliance.
Apply guardrails addressing hallucinations, bias, data leakage, misuse, and handling of confidential, sensitive, and regulated data.

Requirements

4–7 years of relevant experience in data science, AI engineering, or applied machine learning roles.
Bachelor’s or Master’s degree in Computer Science, Mathematics, Engineering, or a related quantitative field.
Strong hands-on experience with Python.
Strong foundation in machine learning, NLP, statistical modeling, data exploration, and data visualization.
Hands-on experience or projects involving LLMs, LangChain, LangGraph, RAG, or agentic AI systems.
Experience delivering AI or machine learning solutions into production environments.
Understanding of text processing and unstructured data.
Knowledge of software engineering practices including test-driven development, refactoring, modular design, and version control.
Strong analytical, problem-solving, and communication skills.
Experience with Azure or Google Cloud Platform is preferred.
Familiarity with DevOps tools and CI/CD pipelines is preferred.
Knowledge of data governance, responsible AI, or model risk management is preferred.
Prior experience in financial services or another regulated environment is preferred.

Transparent compensation schemes and comprehensive location-tailored employee benefits supporting financial security, health, and wellbeing.
Flexible working arrangements, advanced technology, and collaborative workspaces.
Global orientation programming and access to Learning@MSCI, AI Learning Center, LinkedIn Learning Pro, and tailored development opportunities.
Professional growth through multi-directional career paths, internal mobility, and expanded roles.
Inclusive workplace culture with eight Employee Resource Groups and reasonable accommodation support for individuals with disabilities.