Staff Data Scientist - Fraud & Risk

A high-growth technology company is seeking a Staff Data Scientist to join its Fraud & Risk organization. This is an opportunity to work on some of the most challenging machine learning problems in the industry, developing models that drive fraud prevention, risk assessment, and identity intelligence at scale.

This role is ideal for a hands-on data scientist who enjoys owning the full machine learning lifecycle, from research and experimentation through deployment and production monitoring. The successful candidate will partner closely with engineering, product, and business stakeholders to develop innovative solutions that have a direct impact on both customers and the business.

Responsibilities

• Design, build, and deploy advanced machine learning and deep learning models to address fraud, risk, and identity-related challenges.
• Lead the development of solutions leveraging transformers, graph learning algorithms, CNNs, RNNs, and other modern AI techniques.
• Own the end-to-end model development process, including data exploration, feature engineering, training, validation, deployment, and monitoring.
• Work with large-scale structured and unstructured datasets spanning multiple data sources and modalities.
• Conduct research and experimentation to identify new approaches that improve model performance and business outcomes.
• Collaborate with Product, Engineering, and Risk teams to translate business challenges into scalable machine learning solutions.
• Present analytical findings and recommendations to technical and executive stakeholders.
• Mentor junior team members and contribute to a culture of technical excellence and continuous innovation.

Qualifications

• Master's degree, PhD, or equivalent industry experience in Computer Science, Data Science, Statistics, Applied Mathematics, Machine Learning, or a related field.
• 8+ years of experience in Data Science, Machine Learning, Artificial Intelligence, or related disciplines.
• Proven experience developing, deploying, and maintaining machine learning models in production environments.
• Expertise in Python, SQL, and modern machine learning frameworks including PyTorch, TensorFlow, and scikit-learn.
• Strong understanding of machine learning algorithms, model evaluation methodologies, feature engineering, and data pipeline development.
• Hands-on experience developing deep learning models including transformers, graph neural networks, natural language processing models, and computer vision applications.
• Experience working in fast-paced, highly collaborative environments and driving technically complex projects independently.
• Excellent communication skills with the ability to explain sophisticated concepts to both technical and non-technical audiences.

Preferred Experience

• Fraud detection and prevention
• Risk modeling and analytics
• Identity verification
• Financial technology or payments platforms
• Real-time machine learning systems
• Large Language Models (LLMs)
• Agentic AI frameworks
• MLOps and model monitoring
• Graph Neural Networks (GNNs)
• Natural Language Processing (NLP)
• Computer Vision

Key Technologies

Python * SQL * PyTorch * TensorFlow * scikit-learn * Machine Learning * Deep Learning * Transformers * Graph Learning * GNNs * NLP * Computer Vision * LLMs * Agentic AI * LangChain * LangGraph * MLOps * Fraud Detection * Risk Modeling * Identity Intelligence

Please note: Candidates must be authorized to work in the United States without current or future sponsorship requirements.