The Manager – Data Science role is a people‑leader position within the Model Risk Management Group (MRMG) under the Global Risk and Compliance organization. The role is accountable for both independent risk oversight of Generative AI and Advanced ML models and for leading, coaching, and developing a team of analysts supporting enterprise‑wide GenAI model risk activities.
This role oversees LLM‑based systems, agentic AI applications, and advanced ML models across customer servicing, marketing, fraud, credit, customer experience, technology and operations, and risk domains. This role plays a critical part in strengthening enterprise model risk controls while enabling responsible innovation, and will play a key role in shaping MRMG’s GenAI governance capabilities, talent, and operating model.
Success in this role requires a blend of technical depth, risk judgement, people leadership, and executive communication, with strong focus on audit‑readiness and evolving regulatory expectations.
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.
Responsibilities:
GenAI Model Risk Oversight & Governance- Provide independent oversight and effective challenge of Generative AI, LLM‑based, agentic AI, and advanced ML models across the enterprise.
- Lead risk based GenAI model risk reviews, including assessment of:
- Model design and architecture
- Training data and prompt strategies
- Guardrails, monitoring, and control effectiveness
- Explainability, bias, robustness, and misuse risks
- Conduct gap assessments against internal standards and external regulatory expectations, and drive remediation in partnership with model owners.
- Contribute to the design and enhancement of GenAI‑specific MRMG frameworks, guidance, and validation standards.
People Leadership & Talent Development
- Manage, coach, and develop a team of analysts, fostering strong technical capability and risk mindset.
- Provide regular feedback, mentoring, and on‑the‑job training to build GenAI, LLM, and AI‑risk expertise within the team.
- Set clear expectations, prioritize workloads, and ensure high‑quality, timely delivery across multiple concurrent reviews.
- Build a culture of collaboration, accountability, continuous learning, and inclusion.
Thought Leadership & External Perspective
- Stay current on GenAI, AI regulation, and industry best practices, translating insights into actionable MRMG guidance.
- Support the development of enterprise positions on responsible AI, model governance, and regulatory engagement.
- Partner with stakeholders across risk, engineering, data science, product, and legal to support safe and scalable AI adoption.
Stakeholder Communication & Enterprise Engagement
- Communicate complex GenAI model risk findings clearly to senior leadership, model committees, and governance forums.
- Represent MRMG perspectives with confidence while maintaining strong, constructive partnerships.
- Support audits, reviews, and governance forums with clear documentation and defensible risk rationale.
Critical Factor to Success
Business & Enterprise Outcomes
- Elevate enterprise GenAI model excellence by embedding strong governance without stifling innovation.
- Drive consistency, efficiency, and defensibility in GenAI risk management practices.
- Apply external perspective to identify emerging risks and opportunities in AI adoption.
Leadership Behaviors
- Set the Agenda:
- Put enterprise priorities first and define what “winning” looks like for responsible GenAI adoption.
- Bring Others With You:
- Build strong partnerships, foster collaboration, and provide effective challenge with credibility and respect.
- Do It the Right Way:
- Communicate frequently, candidly, and clearly; make sound, timely decisions with integrity.
- Embody American Express values and demonstrate courage in risk stewardship.
Qualifications:
Education
- MBA or Master’s Degree in Statistics, Economics, Data Science, AI/ML, or related quantitative fields from a top‑tier institute.
Experience
- 4+ years of experience in analytics, model development, validation, model risk, or advanced data science roles.
- Prior experience leading or mentoring analysts in a structured, professional environment.
- Hands‑on experience with AI/ML models, with exposure or demonstrated learning in Generative AI or LLM‑based systems strongly preferred.
- Experience engaging with risk, compliance, audit, or regulatory stakeholders is a plus.
Technical Skills
- Strong understanding of AI/ML concepts, including GenAI and LLM fundamentals.
- Experience assessing risks related to data, bias, explainability, performance drift, robustness, and controls.
- Proficiency in at least one of Python, PySpark, R or SQL.
Core Capabilities
- Strong analytical judgement combined with structured risk thinking.
- Excellent written and verbal communication, including the ability to translate technical risk into executive‑level insights.
- Proven ability to manage teams, set priorities, and deliver results in a fast‑changing environment.
