Analyst-Data Science

The Analyst, Data Science (030313) role sits within the Model Risk Management Group (MRMG) under the Global Risk and Compliance organization. The role supports the independent risk management and governance of Generative AI and advanced Machine Learning models across American Express.
This role focuses on the assessment and monitoring of LLMs, GenAI applications, and ML‑based models used in areas such as marketing, credit, fraud, customer engagement, operations, and risk decisioning. The analyst will contribute to strengthening enterprise model risk controls, elevating model excellence, and supporting compliance with evolving regulatory and governance expectations for AI systems.
The role requires strong analytical skills, curiosity in AI/ML technologies, and the ability to translate technical findings into clear, risk‑focused insights for stakeholders.
Education
MBA or Master’s Degree in Statistics, Economics, Data Science, AI/ML, Generative AI or related quantitative fields from a top‑tier institute.
Experience
0–2 years of experience in analytics, data science, model development, validation, or big‑data workstreams.
Exposure to AI/ML model development, testing, or validation through professional experience, projects, or internships preferred.
Interest in or early exposure to Generative AI or LLM‑based systems is a strong plus.
Technical Skills
Foundational understanding of AI/ML concepts, with interest in Generative AI technologies.
Hands‑on experience with at least one of Python, PySpark, R, or SQL.
Ability to work with data, perform analytical checks, and support model evaluation activities.
Core Capabilities
Strong analytical, problem‑solving, and structured‑thinking skills.
Clear written and verbal communication, with ability to explain analytical results to diverse audiences.
Ability to manage multiple tasks, adapt to changing priorities, and meet tight timelines.
GenAI Model Risk Assessment & Oversight
Support independent oversight and effective challenge of Generative AI, LLM‑based, and advanced ML models across the enterprise.
Participate in risk based GenAI model risk reviews, including assessment of:
Model objectives, design, and architecture
Training data, prompt design, and assumptions
Model performance, monitoring approaches, and control mechanisms
Risks related to bias, explainability, robustness, and misuse
Execute model risk testing, documentation reviews, and evidence assessment in line with MRMG standards.
Frameworks, Research & Continuous Learning
Contribute to gap assessments against internal policies and external regulatory expectations for AI/ML models.
Conduct AI/ML and GenAI research to support MRMG guidance, standards, and validation approaches.
Stay current on emerging trends in Generative AI, AI risk management, and regulatory developments, and apply learnings to day‑to‑day work.
Stakeholder Collaboration & Communication
Prepare clear, well‑structured analysis, validation notes, and risk summaries for internal stakeholders.
Communicate analytical findings effectively to business partners, model committees, and senior leaders, with guidance from managers.
Collaborate with cross‑functional teams including data science, engineering, product, and risk partners to support validation execution.
Enterprise Contribution
Support consistent, scalable, and defensible GenAI risk management practices across the enterprise.
Help improve efficiency and quality of MRMG processes through strong analytical execution and documentation discipline.

Work arrangement
Hybrid