Formulate and drive original research directions in graph learning for fraud detection, exploring architectures such as GNNs, graph transformers, and hybrid models
Design scalable approaches for dynamic, heterogeneous, and large-scale fraud graphs
Investigate agentic AI and LLM-augmented systems for automated risk reasoning, investigation workflows, and decision support
Develop robust learning techniques for adversarial and non-stationary environments
Conduct rigorous empirical evaluation on real-world, production-scale datasets
Translate research insights into practical system-level implications in collaboration with data scientists and engineers
Contribute to patent development and preparation of submissions to top-tier academic venues
Requirements
Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, Mathematics, or a related field
Experience scaling graph-based or transformer-based architectures to large datasets (Preferred Qualifications)
Familiarity with graph learning libraries (e.g., PyG, DGL) (Preferred Qualifications)
Experience working with noisy, highly imbalanced, or adversarial datasets (Preferred Qualifications)
Tech Stack
Python
PyTorch
Tensorflow
Benefits
Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.