Analyst-Data Science

The Analyst, Data Science 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.

At American Express, our culture is built on a 175-year history of innovation, shared values and Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues. From delivering differentiated products to providing world-class customer service, we operate with a strong risk mindset, ensuring we continue to uphold our brand promise of trust, security, and service.

As part of Team Amex, you’ll experience our powerful backing with comprehensive support for your holistic well-being and many opportunities to learn new skills, develop as a leader, and grow your career. Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express.

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.

Critical Factors to Success

Business & Enterprise Outcomes

• Contribute to improved model accuracy, robustness, and governance for GenAI and ML models.
• Support enterprise objectives by enabling responsible AI deployment through strong risk discipline.
• Continuously improve technical and domain expertise to enhance business impact.

Enterprise Leadership Behaviors

• Set the Agenda 
  • Demonstrate enterprise thinking and connect work outputs to broader risk and business priorities.
• Bring Others With You 
  • Collaborate effectively, seek feedback, and actively contribute as part of high‑performing teams.
• Do It the Right Way 
  • Communicate clearly and candidly, demonstrate integrity in analysis, and uphold American Express values.
  • Show learning agility, curiosity, and willingness to challenge assumptions responsibly.

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.