Manager-Data Science

3 or more years of Relevant/Related Work Experience in Credit & Fraud Risk with Demonstrated Track Record of Strong Performance.

1. Proficiency in programming with Python, PySpark, SQL on BigQuery, Hive Terminal, MLS
2. Knowledge of platforms systems like Lumi, Cornerstone, CAS, AMP, etc.
3. Preferred - Experience in working with Gen AI applications.

At American Express, our culture is built on a 175-year history of innovation, shared valuesand 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.

Manager – Agentic Risk

The incumbent would be part of the Agentic Risk Modeling team reporting into Director – Agentic Risk Modeling and will lead a team of risk management professionals. The incumbent is expected to provide thought leadership and manage Agentic Risk decision science initiatives involving developing, documenting & implementing state-of-the-art ML Agentic risk prediction models, regularly tracking the performance of these models along with doing case reviews to identify innovative data science opportunities.

Specific responsibilities would include:

1. Develop, document & implement models to capture Agentic risk.
2. Be extremely diligent & regular in tracking the performance of the model on an ongoing basis & propose corrective actions in a timely manner.
3. Partner with Business Strategy, MRMG, RPDS teams to ensure that models are continuously enhanced & stay world-class.
4. Maintain a disciplined process of reviewing cases to identify new modeling opportunities.
5. Evaluate new data sources - internal and external, as well as new variables designed from extensive case reviews for their efficacy in models and business strategies.
6. Be on a continuous look-out for identification of gaps in today’s modeling process & propose / recommend solutions to solve for those gaps and make existing solutions more robust.

Provide people and thought leadership for Band 30s.

Post Graduate Degree in Management / Statistics / Mathematics / Economics / Engineering from a reputed institute.