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# Manager, Data Science

- **Company:** American Express Company
- **Location:** New York, NY, United States
- **Salary:** $103,750.0 - $174,750.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Amazon Web Services, Artificial Neural Networks, Big Data, C++ (Programming Language), Fraud Prevention and Detection, Apache Hadoop, Apache Hive, Python (Programming Language), Machine Learning, Scala (Programming Language), Performance Testing, Apache Spark, Deep Learning, Xgboost
- **Published:** October 15, 2026
- **Apply:** https://www.manhattanjobs.com/job.asp?id=3448911308&tx=HT7872TYD&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* 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.

## Description

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