Senior Data Science Engineer

Your Team Responsibilities:

MSCI Data Tech - Data Science is a central team responsible for building and managing data-centric and AI-driven applications and capabilities that support all major business verticals across the firm, including ESG, Index, Analytics, and Real Estate. The team enables high-quality financial decision-making by delivering scalable, reliable, and compliant AI solutions used by some of the world's largest financial services organizations.

We are seeking AI Engineers at Senior Associate levels to support MSCI's AI adoption journey, with a focus on machine learning, NLP, GenAI, LLMs, and agentic AI systems. These roles are hands-on and engineering-focused, centered on building, integrating, and operationalizing AI capabilities across internal platforms and client-facing products.

AI & Data Science Application Development:

• Design, develop, and maintain data science and AI-driven applications that support multiple MSCI business lines.
• Implement machine learning, NLP, and GenAI solutions that move from experimentation to production.
• Build and integrate LLM-powered capabilities such as retrieval-augmented generation (RAG), summarization, classification, extraction, and reasoning into enterprise platforms.

GenAI, LLMs & Agentic AI:

• Develop and enhance solutions using LLMs, LangChain, LangGraph, and agentic AI frameworks.
• Apply best practices in prompt engineering, orchestration, evaluation, and monitoring to ensure consistent, reliable AI outputs.
• Work with structured and unstructured data, including text processing, embeddings, and vector-based retrieval.

Platform Integration & Cross-Functional Collaboration:

• Support teams across Data, Technology, and Engineering to integrate AI services with MSCI's data science platforms and APIs.
• Engage with content expert teams to understand MSCI datasets, data delivery platforms, and business workflows.
• Collaborate closely with Product Management, Quality Assurance, Data Operations, and IT Infrastructure across all stages of the development lifecycle.

Production Readiness & Engineering Excellence:

• Ensure AI solutions meet enterprise standards for scalability, performance, reliability, and maintainability.
• Follow strong software engineering best practices, including test-driven development, code reviews, refactoring, and clean system design.
• Contribute to CI/CD pipelines and DevOps processes to enable continuous integration, deployment, and monitoring of AI systems.

AI Governance, Risk & Compliance:

• Ensure AI and GenAI solutions comply with MSCI's data governance, security, and responsible AI standards.
• Implement controls to support model transparency, traceability, auditability, and reproducibility, especially for LLM-based systems.
• Apply guardrails to mitigate hallucinations, bias, data leakage, and misuse in AI outputs.
• Ensure appropriate handling of confidential, sensitive, and regulated data in accordance with firm policies.
• Partner with Risk, Legal, Compliance, and Information Security teams to support reviews, audits, and regulatory requirements related to AI systems.

Your skills and experience that will help you excel:

Required:

• 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 in Python.
• Solid foundation in machine learning, NLP, and statistical modeling, including data exploration and visualization.
• Hands-on experience or projects involving LLMs, LangChain, LangGraph, RAG, or agentic AI systems.
• Experience delivering AI/ML solutions into production environments.
• Understanding of text processing and unstructured data.
• Knowledge of software engineering best practices (TDD, refactoring, modular design, version control).
• Strong analytical, problem-solving, and communication skills.
• Self-motivated, proactive, and delivery-oriented mindset.

Preferred / Nice to Have:

• Experience with cloud platforms, particularly Azure or Google Cloud Platform (GCP).
• Familiarity with DevOps tools and CI/CD pipelines.
• Understanding of data governance, responsible AI, or model risk management concepts.
• Prior experience in financial services or other regulated environments.