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Senior Credit Card Fraud Analyst

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Key Roles and Responsibilities:

Job Summary: Senior Fraud Risk Modeler(Credit Card)

We seek a highly experienced Senior Fraud Risk Modeler to join our team. The successful candidate will be responsible for designing, developing and maintaining models to detect and prevent fraud across various financial products and channels - primarily in the credit card domain.

Main Responsibilities:
  • Model Development & Validation: Build, implement and validate fraud detection and prevention models using advanced statistical and machine learning techniques for credit card products.
  • Data Analysis & Interpretation: Analyze large-scale transactional and behavioral data to identify fraud patterns, assess model performance and provide actionable insights.
  • Risk Assessment & Monitoring: Evaluate existing fraud controls, monitor fraud trends and quantify exposure to evolving threats across digital and traditional channels.
  • Communication & Collaboration: Effectively present analytical findings and model performance to both technical and non-technical stakeholders, including fraud operations, compliance and executive leadership.
  • Documentation & Reporting: Maintain comprehensive documentation of model development processes, risk assumptions, fraud detection frameworks and compliance requirements.
  • Technical Skills: Strong proficiency in Python, SQL and data science tools; familiarity with real-time decision engines and fraud detection platforms is a plus. (e.g., Actimize, Falcon, SAS).
  • Domain Expertise: Deep understanding of fraud domain expertise for debit card product.
  • Leadership & Teamwork: Ability to lead fraud analytics initiatives, mentor junior analysts or data scientists and collaborate with risk, IT and fraud operations teams.
  • Problem Solving: Proactively identify vulnerabilities in fraud strategies, recommend enhancements and respond to emerging threats in a fast-paced environment.
Requirements:

The ideal candidate should have at least 6 years of hands-on experience in a banking environment, specifically in developing, implementing and maintaining fraud risk models across products and channels. A Bachelor's or Master's degree in a quantitative discipline such as Statistics, Mathematics, Computer Science, Economics, Data Science or a related field is required. Additionally, the following skills are essential: strong programming skills in Python, SQL, R or SAS for model development, data manipulation and automation; proficiency with machine learning and statistical techniques such as decision trees, gradient boosting, clustering, anomaly detection and neural networks; good understanding of how linear regression and XGBoost work, including their assumptions, strengths and limitations; solid understanding of fraud typologies and fraud prevention technologies; demonstrated ability to work with large and complex datasets, conduct deep-dive analytics and extract actionable insights; excellent communication and stakeholder management skills, with the ability to clearly articulate model logic, assumptions and outcomes to non-technical audiences; experience performing fraud model validation, performance monitoring and tuning processes to ensure effectiveness and compliance; strong problem-solving skills to address emerging fraud risks, data quality challenges and operational issues.