- Develop and maintain transaction models with a strong focus on identifying and mitigating fraud risks.

- Design and implement fraud detection models using advanced statistical and machine learning techniques.

- Perform data extraction, transformation, and analysis using SQL and Python; SAS experience is a plus.

- Utilize and apply Logistic Regression, Random Forest, Gradient Boosting, and other supervised learning techniques to detect fraud patterns.

- Leverage statistical analysis to interpret complex datasets and derive actionable insights for fraud prevention.

- Collaborate with cross-functional teams including Risk, Compliance, and Engineering to deploy fraud analytics solutions.

Required Skills :

- Proven experience in fraud analytics, specifically in transaction modeling.

- Strong command of SQL and Python for data manipulation and model development.

- Experience with machine learning algorithms, especially:

- Logistic Regression

- Random Forest

- Gradient Boosting

- Solid foundation in statistics and data-driven decision making.

- Experience in model development, validation, and deployment.