• Monitor transactional data to identify suspicious patterns, emerging fraud trends and anomalies.
• Design and maintain fraud detection dashboards, models, and rules using statistical and machine learning techniques.
• Collaborate with product, engineering, and compliance teams to implement and fine-tune fraud detection.
• Conduct root cause analyses on fraud incidents and provide actionable insights.
• Develop reporting frameworks to communicate fraud trends, risk metrics, and investigation outcomes to stakeholders.
• Continuously refine detection logic based on feedback and fraud evolution.
• Support incident response teams in investigating and responding to fraud alerts.
• Stay current with industry best practices and evolving fraud tactics.

• Bachelor’s degree
• 2-4+ years of experience in fraud analytics, risk management, or a data analyst role with fraud exposure.
• Strong proficiency in SQL for data extraction, transformation, and analysis.
• Hands-on experience with data tools like Python, R, or similar analytics languages.
• Familiarity with BI tools such as Tableau, Power BI, or Looker.
• Knowledge of fraud detection systems, scoring models, and behavioral analytics.
• Experience working with large datasets and cloud-based data platforms (e.g., Snowflake, BigQuery, Redshift).
• Understanding of payment systems, chargeback processes, or identity verification methods is a plus.
• Exposure to machine learning libraries for anomaly detection.
• Experience with fraud case management tools and rule engines.
• Background in e-commerce, fintech, or banking sectors.
• Ability to communicate complex insights clearly to both technical and non-technical stakeholders.