Senior Fraud Data Scientist — ML & Detection Strategy

Selby Jennings; New York (NY); On-site; USD 140,000 - 165,000; Full time

Job summary

Selby Jennings, a leading consulting firm, is seeking a Fraud Analytics Senior Associate/Data Scientist in New York to help clients across financial services, fintech, and payments strengthen fraud detection capabilities. You will work on fraud strategy, building and deploying models, and delivering actionable insights to stakeholders.

The role focuses on developing machine learning driven fraud detection models, optimizing detection frameworks, monitoring model performance, and communicating

Qualifications

• 3-6 years in Fraud Analytics, Fraud Strategy, Fraud Data Science, or Financial Crimes Analytics.
• Background in financial services, fintech, payments, and other fraud-intensive industries
• Experience developing machine learning-driven fraud detection models focused on identifying suspicious activity and reducing false positives.

Responsibilities

• Develop and enhance fraud detection strategies using rules-based and machine learning approaches.
• Build, deploy, and maintain fraud models and analytics solutions using Python and SQL.
• Design and implement fraud detection frameworks to identify suspicious activity and emerging fraud patterns.
• Monitor model performance, including accuracy, drift, false positives, and ongoing optimization.
• Partner with business and risk stakeholders to identify fraud vulnerabilities and improve detection capabilities.
• Communicate analytical insights and model results to both technical and non-technical audiences.

Skills

Fraud analytics

Fraud strategy

Financial crimes analytics

Python

SQL

Machine learning

Communication skills

Tools

Python

SQL

ML frameworks

Job description

Selby Jennings, a leading consulting firm, is seeking a Fraud Analytics Senior Associate/Data Scientist in New York to help clients across financial services, fintech, and payments strengthen fraud detection capabilities. You will work on fraud strategy, building and deploying models, and delivering actionable insights to stakeholders.

The role focuses on developing machine learning driven fraud detection models, optimizing detection frameworks, monitoring model performance, and communicating