Credit Risk Data Analyst

Job Summary

Amazon is seeking a highly analytical and detail-oriented Credit Risk Data Analyst – Fraud Detection to join our risk and compliance analytics team. In this role, you will play a critical part in protecting Amazons customers, merchants, and financial ecosystem by identifying, analyzing, and mitigating fraud and credit risk through advanced data analytics.

You will work with large-scale datasets, develop predictive models, and collaborate with cross-functional teams to design data-driven solutions that prevent fraud while maintaining a seamless customer experience. This position is ideal for professionals who are passionate about analytics, risk management, and using technology to solve complex business problems at scale.

Key Responsibilities

- Analyze large, complex datasets to identify fraud patterns, credit risk trends, and emerging threats.
- Develop, validate, and maintain fraud detection and credit risk models using Python and/or R.
- Design dashboards, reports, and automated alerts to monitor fraud performance and risk metrics.
- Partner with product, engineering, finance, and compliance teams to translate analytical insights into actionable strategies.
- Conduct root cause analysis on fraud incidents and recommend process or system improvements.
- Continuously improve model accuracy through feature engineering, testing, and performance monitoring.
- Support regulatory and internal audit requirements by providing clear documentation and data insights.
- Contribute to the development of scalable analytics frameworks that support Amazons global operations.

Required Skills and Qualifications

- Strong proficiency in Python and/or R for data analysis, modeling, and automation.
- Solid experience with SQL and relational databases.
- Strong understanding of statistical analysis, predictive modeling, and machine learning concepts.
- Experience working with large datasets and big data tools (e.g., AWS, Spark, or similar platforms).
- High attention to detail with strong problem-solving and critical-thinking skills.

Experience

- Bachelors degree in Data Science, Statistics, Mathematics, Economics, Computer Science, Finance, or a related field.
- 2–5 years of experience in data analytics, credit risk, fraud detection, or financial risk management.
- Prior experience in e-commerce, fintech, banking, or payments is highly desirable.
- Hands-on experience building and deploying fraud or risk models in a production environment is a strong advantage.

Working Hours

- Full-time position.
- Flexible working hours aligned with business needs and global team collaboration.
- Remote or hybrid work options may be available depending on location and team requirements.

Knowledge, Skills, and Abilities

- In-depth knowledge of fraud detection methodologies and credit risk frameworks.
- Ability to work independently in a fast-paced, ambiguous environment.
- Strong business acumen with the ability to align analytics with customer and company goals.
- Excellent written and verbal communication skills.
- Commitment to continuous learning and staying current with analytics and fraud prevention trends.

Benefits

- Competitive salary and performance-based incentives.
- Comprehensive health, dental, and vision insurance.
- Retirement savings plans with company contributions.
- Paid time off, holidays, and wellness programs.
- Career development opportunities, training, and access to Amazons internal learning platforms.
- Employee discounts and additional Amazon-specific perks.

Why Join Amazon

At Amazon, we are driven by innovation, customer obsession, and data-backed decision-making. Joining Amazon means working on problems at an unmatched scale, collaborating with some of the brightest minds in the industry, and making a real impact on millions of customers worldwide. We foster an inclusive culture that values diversity, continuous improvement, and bold thinking.

How to Apply

Interested candidates are encouraged to apply through Amazons official careers portal. Please submit your updated resume along with a brief cover letter highlighting your experience in data analytics, fraud detection, and credit risk. Qualified candidates will be contacted for further assessment and interviews.

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