Manager, Data Science

The Model Risk Management Group (MRMG) is responsible for oversight of model risk and governance over machine learning usage enterprise wide. The role will be primarily responsible for the review of credit risk, fraud risk, and line management models. The responsibilities of the individual are also to ensure that there is an elevation of modeling as well as fulfilling regulatory requirements for model risk management.

This is a highly competitive role and one that ensures the individual achieves maximum potential intellectually as well as for the company.

Qualifications

• Advanced degree in quantitative fields
• Hands-on modeling experience is preferred
• The qualified candidate should have a good understanding of machine learning algorithms (e.g.,     gradient boosting machine, XGBoost, neural network), artificial intelligence, deep learning as well   as classic statistical modeling techniques and assumptions.
• Experience in large data processing and handling is a plus—familiarity with big data platforms and   applications, such as Hadoop, Pig, Hive, Spark, AWS.
• Must be skilled in coding using Python/R; additionally, Java, Scala, and C++ a plus.
• Strong communication and written skills
• Flexibility and adaptability to work within tight deadlines and changing priorities
• Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.

Responsibilities

• This role will report to a director in MRMG. The specific responsibilities include: Oversee credit risk, fraud risk, and line management models which include evaluating conceptual soundness, data quality, performance validation, and on-going monitoring
• Document detailed model review reports and prepare for regulatory and internal audit reviews
• Communicate model review results to business partners, senior leaders, and regulators
• Evaluate machine learning algorithms and their application on models
• Enhance governance framework to accommodate the evolving modeling techniques, model development, as well as model risk management