Staff Data Scientist - Fraud & Risk

Socure is building the identity trust infrastructure for the digital economy, focusing on verifying identities and preventing fraud. The Staff Data Scientist will design and optimize advanced deep learning models for fraud detection and risk management while leading technical initiatives and mentoring peers.

Responsibilities:

• Design, develop, and implement advanced deep learning models, including transformers, CNNs/RNNs, and graph learning algorithms, to address complex fraud and risk challenges
• Build and optimize models using a variety of input data types, including tabular data, natural language, point clouds, and images
• Lead the end-to-end machine learning lifecycle: data exploration, feature engineering, model training, evaluation, deployment, and monitoring in production environments
• Take ownership of project outcomes, data quality, and delivery timelines; proactively escalate issues and work collaboratively to resolve challenges
• Mentor and share knowledge with peers and junior data scientists, fostering a culture of experimentation, rapid iteration, and continuous learning
• Collaborate cross-functionally with Product, Engineering, and Risk teams to define data requirements and drive insights that guide strategic decisions
• Conduct in-depth research to explore new data sources and develop novel algorithms that advance the state of the art in fraud detection
• Present findings and recommendations to technical and executive stakeholders with clarity and influence
• Stay current with advancements in AI and machine learning, applying innovative approaches to real-world problems
• Model Socure’s embedded leadership competencies: continuous learning, effective communication, accountability, team development, decision making, and managing change