Data Science

Summary: The role focuses on fraud analytics, emphasizing fraud detection and mitigation through advanced analytics and modeling techniques. Candidates should have experience in building and deploying machine learning models in a production environment, particularly within banking or fintech. Proficiency in programming languages and data science libraries is essential, along with exposure to real-time fraud systems and large datasets. The position is remote, targeting candidates preferably located on the East or West Coast of the USA, excluding those based in Los Angeles, California.

Key Responsibilities:

• Fraud detection and mitigation using advanced analytics and modeling.
• Building and deploying machine learning models in a production fraud detection environment.
• Utilizing graph analytics, behavioral biometrics, and NLP for financial crime risk management.
• Working with large datasets and cloud platforms such as AWS, Google Cloud Platform, and Azure.
• Exposure to real-time fraud systems like SAS Fraud Management, Actimize, and Falcon.

Key Skills:

• Strong command of Python, R, SQL, and data science libraries (pandas, scikit-learn, TensorFlow, etc.).
• Experience in financial crime risk management within banking or fintech.
• Familiarity with Hadoop/Spark for handling large datasets.
• Experience with real-time fraud detection systems.

Salary (Rate): undetermined

City: undetermined

Country: USA

Working Arrangements: remote

IR35 Status: outside IR35

Seniority Level: undetermined

Industry: IT