Senior Credit Card Fraud Analyst

2 days ago

Vadodara, Gujarat, India beBeeFraud Full time ₹ 1,20,00,000 - ₹ 2,00,00,000

Job Summary

We are seeking an experienced professional to join our team as a Senior Fraud Risk Modeler. As a key member of our risk management team, you will be responsible for developing and implementing advanced statistical and machine learning models to prevent fraud in credit card products.

Responsibilities:

  • Fraud Detection and Prevention: Develop and implement machine learning models to identify fraudulent transactions and prevent financial losses.
  • Data Analysis and Interpretation: Analyze large-scale transactional data to identify trends and patterns that indicate potential fraud.
  • Risk Assessment and Monitoring: Evaluate existing fraud controls and monitor emerging threats to ensure the integrity of our systems.
  • Communication and Stakeholder Management: Collaborate with cross-functional teams to present analytical findings and model performance to stakeholders.
  • Documentation and Reporting: Maintain comprehensive documentation of model development processes and risk assumptions.
  • Technical Skills: Strong proficiency in Python, SQL, and data science tools.
  • Domain Expertise: Deep understanding of credit card fraud domain expertise.
  • Leadership and Teamwork: Ability to lead fraud analytics initiatives and mentor junior analysts or data scientists.
  • Problem Solving: Proactively identify vulnerabilities in fraud strategies and recommend enhancements.
  • Adaptability: Stay current with the latest fraud schemes, detection technologies, and regulatory developments impacting fraud risk management.

Requirements:

  • Bachelor's or Master's degree in a quantitative discipline such as Statistics, Mathematics, Computer Science, Economics, Data Science, or a related field.
  • 6-7 years of hands-on experience in a banking environment, specifically in developing, implementing, and maintaining fraud risk models across products and channels.
  • Proven experience in fraud analytics and modelling, including transaction monitoring, real-time detection, and rules-based or machine learning-driven approaches.
  • 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.

About Us

We are committed to fostering a collaborative and inclusive work environment that promotes innovation and excellence.

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