American Express – Analyst Data Science

Job Description

American Express is hiring an Analyst – Data Science to join its Model Risk Management Group (MRMG) within the Global Risk Banking and Compliance division in Gurugram. As one of the world’s most recognized financial services organizations, American Express uses advanced analytics, artificial intelligence, and machine learning technologies to drive strategic decision-making and manage risk effectively. In this role, the Data Science Analyst will help evaluate, monitor, and manage model risk associated with enterprise-wide predictive models used for credit risk, fraud detection, marketing analytics, and other business applications.

The position focuses on ensuring that machine learning and artificial intelligence models used across the organization meet strict regulatory and performance standards. The analyst will conduct independent reviews of models, identify potential gaps in model governance, and recommend improvements to strengthen model validation processes. By working with large datasets and applying advanced statistical techniques, the professional will support the development of robust frameworks that ensure model accuracy, reliability, and compliance with industry regulations.

In addition to model validation responsibilities, the role requires strong collaboration with cross-functional teams including data scientists, risk managers, and business stakeholders. The analyst will present analytical findings, communicate complex insights to leadership teams, and contribute to research initiatives that explore emerging trends in AI and machine learning. This opportunity offers an excellent platform for professionals interested in financial analytics, model risk management, and advanced data science technologies within a global financial institution.

Roles & Responsibilities

1. Conduct independent oversight and validation of enterprise-wide predictive models used for credit risk, fraud detection, marketing analytics, and other business functions.

2. Evaluate artificial intelligence and machine learning models to ensure compliance with internal governance policies and external regulatory standards.

3. Perform gap assessments to identify weaknesses in model development, validation frameworks, and risk management processes.

4. Apply advanced statistical and quantitative techniques to evaluate model performance and identify potential model risks.

5. Collaborate with data science teams and risk management stakeholders to strengthen model governance frameworks across the organization.

6. Conduct research on emerging technologies in artificial intelligence and machine learning to support innovation and regulatory readiness.

7. Analyze large datasets using advanced data manipulation tools and statistical methods to derive insights that improve model reliability.

8. Prepare analytical reports and present findings to business partners, risk committees, and senior leadership teams.

9. Support the implementation of risk control frameworks that ensure responsible and compliant use of AI and machine learning models.

10. Work closely with cross-functional teams to drive model validation initiatives and ensure effective project execution.

Requirements & Eligibility

1. Master’s degree or MBA in Statistics, Economics, Data Science, Mathematics, Computer Science, or a related quantitative discipline from a reputed institution.

2. 0 to 4 years of experience in data analytics, data science, machine learning, or big data environments.

3. Hands-on experience in data manipulation and analysis using programming languages such as Python, R, SQL, or SAS.

4. Understanding of machine learning algorithms, statistical modeling techniques, and predictive analytics.

5. Knowledge of model validation methodologies and model risk management practices within financial institutions is preferred.

6. Strong analytical thinking and problem-solving abilities with the capability to analyze complex datasets and derive insights.

7. Excellent communication and presentation skills to explain technical results to business stakeholders and senior management.

8. Ability to collaborate effectively with cross-functional teams including data scientists, risk managers, and technology teams.

9. Strong project management and organizational skills to manage multiple validation initiatives simultaneously.

10. Flexibility to work in fast-paced environments and adapt to changing regulatory requirements and business priorities.

Expected Salary

The typical salary for a Data Science Analyst at global financial institutions in India generally ranges between ₹12 LPA to ₹22 LPA, depending on the candidate’s educational background, technical expertise in machine learning, and experience with analytical tools. Companies like American Express often provide additional benefits such as performance bonuses, healthcare coverage, professional training programs, and long-term career development opportunities.

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