• Design, develop, and implement advanced deep learning models, including transformers, CNNs, and LSTMs, 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. • Take ownership of assigned tasks, executing technical and functional activities to support project goals with minimal supervision. • Participate in all stages of the machine learning lifecycle: data exploration, feature engineering, model training, evaluation, and deployment. • Collaborate effectively across teams, sharing knowledge and learning from diverse perspectives to drive results. • Make routine technical decisions and contribute to functional objectives through productive and proactive engagement. • Stay current with advancements in AI and machine learning, applying innovative approaches to real-world problems. • Communicate results and insights clearly to both technical and non-technical audiences.