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Socure is building identity trust infrastructure for the digital economy, verifying identities in real time and preventing fraud. The company operates at the intersection of identity verification, fraud detection, and risk management, serving businesses, governments, and millions of end users.
You will join the Fraud & Risk Data Science team as an individual contributor with autonomy in your day-to-day work. This is a hands-on role focused on designing, developing, and deploying advanced deep learning models to solve complex fraud and risk challenges.
Key responsibilities include:
- Design and implement advanced deep learning models (transformers, CNNs, LSTMs) for fraud detection and risk management
- Build and optimize models across diverse data modalities: tabular data, natural language, point clouds, and images
- Own assigned tasks end-to-end with minimal supervision, executing technical and functional activities
- Participate in the full ML lifecycle: data exploration, feature engineering, model training, evaluation, and deployment
- Collaborate across teams, sharing knowledge and learning from diverse perspectives
- Make routine technical decisions and contribute to functional objectives
- Stay current with AI/ML advancements and apply innovative approaches to real-world problems
- Communicate results and insights clearly to both technical and non-technical audiences
You will work in a mission-driven, fast-paced environment where ownership, critical thinking, and precision problem-solving are valued. The team is high-performing and supportive, with opportunities to contribute to diverse projects.
QUALIFICATIONS:
- Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or related field (with substantial related experience), or Master's degree with relevant experience, or equivalent work background
- 2–4 years of hands-on experience developing and deploying deep learning models (transformers, CNNs, LSTMs)
- Experience working with diverse data modalities: tabular data, text/language, point clouds, images
- Proficiency in Python and major ML libraries/frameworks (PyTorch, TensorFlow, scikit-learn)
- Foundational understanding of ML algorithms, model evaluation techniques, and data pipeline development
- Experience with model deployment and monitoring in production environments (real-time model inferencing is a plus)
- Experience with LLMs and Agentic AI frameworks/infrastructure (LangChain, LangGraph, Ray) is a plus
- Strong problem-solving skills; ability to work independently on straightforward tasks and contribute effectively to project objectives
- Demonstrated ability to collaborate in diverse, cross-functional team environments
- Excellent written and verbal communication skills
- Experience in fraud prevention, risk modeling, or identity verification is a plus
