Senior Fraud Data Scientist (Hybrid)

Green Dot; Los Angeles (CA); Hybrid; USD 121,000 - 181,000; Full time

Job summary

Green Dot is seeking a Fraud Data Scientist in Los Angeles to leverage data analytics, statistical modeling, and fraud detection techniques. You will partner with Fraud Strategy, Operations, Product, Technology, and CES to develop insights, optimize customer experience, and improve fraud detection capabilities.

The role requires analyzing data, developing models and dashboards, and communicating findings to both technical and non-technical audiences.

Qualifications

• Bachelor's degree in Statistics, Mathematics, Computer Science, Engineering, Data Science, or a related field.
• 5–8 years of experience in data science, analytics, fraud analytics, risk analytics, or a related field.
• Experience working with analytical tools and programming languages such as SQL, Python, SAS, or similar technologies.
• Experience performing statistical analysis, data mining, segmentation, trend analysis, and model evaluation.

Responsibilities

• Analyzing data to identify fraud patterns, suspicious activity, and emerging fraud trends.
• Supporting the development, testing, implementation, and ongoing optimization of fraud rules and detection strategies.
• Performing exploratory data analysis using structured and unstructured data sources to support fraud investigations.
• Developing analytical models, scorecards, and reporting solutions that improve visibility into fraud performance.

Skills

Data analysis

Communication

Team collaboration

Education

Bachelor's in a related field

Tools

SQL

Python

SAS

Job description

Green Dot is seeking a Fraud Data Scientist in Los Angeles to leverage data analytics, statistical modeling, and fraud detection techniques. You will partner with Fraud Strategy, Operations, Product, Technology, and CES to develop insights, optimize customer experience, and improve fraud detection capabilities.

The role requires analyzing data, developing models and dashboards, and communicating findings to both technical and non-technical audiences.