Description:

Focus on fraud prevention by designing and developing models to identify anomalous behaviors, suspicious transactions, and fraud-related patterns.
Work with large datasets and apply supervised and unsupervised machine learning techniques to enhance fraud detection systems and automate decision-making.

Requirements:

Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, or a related field with strong programming skills [MANDATORY].
1–2 years of experience in Data Science or Data/Business Analytics (with knowledge of machine learning) [MANDATORY].
Over 2 years of experience using Python and SQL [MANDATORY].
Knowledge in fraud detection, anomaly detection, or modeling with unbalanced datasets [MANDATORY].
Experience in fintech, insurance, or banking is valued but not required [DESIRABLE].
Knowledge of AWS services is a plus [DESIRABLE].
Strong analytical skills and problem-solving mindset, with the ability to extract actionable insights from data [MANDATORY].
Understanding of consumer behavior, alternative data sources, and digital lending platforms [DESIRABLE].

Opportunity to work in a dynamic environment focused on fraud prevention.
Collaborate with cross-functional teams to integrate models into decision flows.

Skills