Manager, Data Analytics
American ExpressLead analytics, performance management, and portfolio strategy for American Express commercial client management. Advise senior leaders on growth, forecasting, and data-informed decisions.
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American Express is a global financial services company operating across banking, finance, and B2B payments. Its business includes consumer, business, and corporate credit cards; payment and merchant services; savings accounts, certificates of deposit, and personal loans; as well as rewards, travel programs, corporate payment solutions, financial education, security services, and cardmember and merchant support.
Senior Data Science Analyst, Fraud Risk
Build machine learning and graph-based solutions to detect payment card compromise and strengthen fraud risk decisions at American Express. Analyze compromised-card data and develop scalable decisioning pipelines.
Description
- Build and improve machine learning and AI solutions that identify card compromise patterns, detect suspicious behavior, and support risk decisions.
- Use Graph Neural Networks, graph learning, Transformers, deep learning, and other advanced machine learning methods.
- Analyze large, complex, interconnected datasets to find relationships and risk signals and produce actionable business insights.
- Build and optimize analytical and modeling pipelines, from data preparation and feature engineering through training, validation, and performance monitoring.
- Use Python, PySpark, and cloud platforms such as Google Cloud to create scalable analytical solutions.
- Evaluate emerging AI and machine learning methods to improve model effectiveness, predictive accuracy, and operational efficiency.
- Work with cross-functional stakeholders to turn business challenges into data-driven solutions.
- Present analytical findings, model performance, and strategic recommendations to technical and non-technical audiences.
- Review cases in depth to improve model performance and decision-making.
- Keep analytical and modeling work aligned with business objectives and manage stakeholder expectations throughout each project.
- Review research papers and identify use cases that can advance model development and innovation.
Requirements
- Hold a graduate or postgraduate degree in statistics, mathematics, economics, engineering, management, computer science, or a related quantitative field from a recognized institution.
- Have 2–3 years of relevant experience in data science, advanced analytics, machine learning, or predictive modeling.
- Bring strong Python and PySpark programming skills, with practical experience in data manipulation, statistical analysis, and machine learning model development.
- Understand machine learning and deep learning techniques, with exposure to Graph Neural Networks, graph algorithms, Transformer architectures, or related advanced AI methods.
- Be familiar with deep learning frameworks such as PyTorch or TensorFlow and cloud environments such as Google Cloud Platform.
- Have experience with large datasets, feature engineering, data preprocessing, and model evaluation.
- Demonstrate strong problem-solving and analytical skills, and the ability to turn complex business requirements into effective technical solutions.
- Communicate clearly in writing and speech, and present analytical insights and recommendations to varied stakeholders.
- Use sound judgment, collaborate effectively, and deliver high-quality analytical work in a dynamic business environment.
- Work the following shifts: Monday–Wednesday, 1:00 PM–9:30 PM; Thursday, 11:00 AM–7:30 PM; Friday, 8:30 AM–5:00 PM.
Benefits
- Competitive base salary.
- Bonus incentives.
- Support for financial well-being and retirement.
- Medical, dental, vision, life insurance, and disability coverage, depending on location.
- Flexible hybrid, onsite, or virtual working arrangements, depending on role and business needs.
- Paid parental leave, depending on location.
- Free access to global onsite wellness centers staffed by nurses and doctors, depending on location.
- Free, confidential counseling through the Healthy Minds program.
- Career development and training opportunities.
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