• 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.