SrAnalyst Data Governance&Mgmt

About Credit and Fraud Risk Credit and Fraud Risk (CFR) is a global function responsible for making effective credit and fraud risk decisions that uphold operational excellence, enable sustainable growth, and accelerate innovation across American Express. CFR brings together advanced technology, data, and analytical capabilities to strengthen risk decision-making, enable real-time customer communications, and support fraud servicing. The team plays an important role in protecting our customers and the company while enabling responsible business growth.Description of the Role

We are looking for a Senior Analyst – Data Governance & Management to support the development, and execution of data capabilities that support customer management risk models for the U.S. market like CDSS and TSR.

In this role, you will help ensure that high-quality, reliable, and accessible data is available to support model development, performance monitoring, and ongoing analytical needs.

You will work at the intersection of data, analytics, risk management, collaborating with cross-functional teams to improve how data is managed, governed, and consumed.

A key focus of the role will be driving innovation in the data ecosystem. You will have opportunities to leverage technologies such as GenAI, BigQuery and Python, along with automation, to simplify data access, reduce manual effort, improve scalability, and develop more efficient and cost-effective data solutions. The role provides an opportunity to solve complex and evolving data problems while contributing to the continued modernization of American Express' risk and analytical capabilities.

• Develop, review, and maintain data capabilities supporting customer management and risk models, including datasets used for model development, execution, and ongoing performance monitoring.
• Analyze large and complex datasets to identify meaningful patterns, trends, data quality issues, and opportunities for improvement.
• Identify opportunities to automate manual processes, simplify existing workflows, and improve operational efficiency.
• Identify opportunities to optimize data capabilities and reduce unnecessary storage, processing, and maintenance costs.
• Explore innovative approaches across data, analytics, automation, and machine learning to continuously improve existing capabilities.
• Partner with cross-functional teams across risk, analytics, technology, and other business areas to improve data accessibility, quality, governance, and usability.
• Structure and clearly communicate analytical findings, recommendations, risks, and business impact to leadership and key stakeholders.
• Navigate complex and unstructured problems by asking thoughtful questions, evaluating different approaches, and translating findings into actionable solutions.

Minimum Qualifications

• MBA or Master's degree in Economics, Statistics, Computer Science, Data Science, or a related quantitative field.
• 0–30 months of relevant experience in analytics, data management, data governance, or related data capabilities.
• Working knowledge of BigQuery and Python, with the ability to analyze and work with large datasets.
• Understanding of data analysis concepts and the ability to translate data into meaningful business insights.
• Ability to manage project deliverables and drive initiatives toward measurable business outcomes.
• Strong analytical and problem-solving skills, with the ability to work through complex and unstructured problems.
• Ability to work independently and take ownership of assigned deliverables while contributing effectively within a team environment.
• Strong written and verbal communication skills, with the ability to clearly communicate findings to both technical and non-technical stakeholders.Preferred Qualifications
• Hands-on experience with BigQuery, Python, SQL, or similar data and analytical technologies.
• Experience developing or supporting automated data processes and workflows.
• Exposure to data governance, data quality, metadata management, data lineage, or data lifecycle management.
• Understanding of model development, model performance monitoring, or the data requirements associated with analytical and machine learning models.
• Exposure to financial services, payments, credit risk, fraud risk, or customer analytics.