Staff Data Scientist - Fraud & Risk

• 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

• Master’s or PhD in Computer Science, Statistics, Applied Mathematics, Data Science, or a related field
• or equivalent professional experience
• 8+ years of experience in data science, machine learning, or related fields, ideally in a high-growth tech or fintech environment
• Experience in fraud prevention, risk modeling, or identity verification
• Years of hands-on experience developing and deploying deep learning models (such as transformers, CNNs/RNNs, and graph learning)
• Experience working with diverse data modalities, such as tabular data, text/language, point clouds, and images
• Strong proficiency in Python, SQL, and major ML libraries/frameworks (e.g., PyTorch, TensorFlow, scikit-learn)
• Deep understanding of machine learning algorithms, model evaluation techniques, and data pipeline development
• Experience with model deployment and monitoring in production environments (specific experience with real-time model inferencing is a plus)
• Demonstrated ability to proactively deliver complex outcomes, mentor others, and influence cross-functional decisions
• Excellent communication skills with the ability to translate complex data problems into actionable business insights for both technical and non-technical audiences
• Commitment to continuous learning, professional integrity, and high standards of business ethics

Experience with LLMs and Agentic AI framework/infrastructure (e.g., LangChain/LangGraph/Ray) is a plus

• Offers Equity
• Offers Bonus