Data Scientist II, Fraud & Risk

Role Overview

We are seeking a skilled and motivated Data Scientist II to join our Fraud & Risk Data Science team. As an individual contributor, you will have autonomy in your day-to-day work and play an important role in driving functional productivity and project success. You will work hands-on with advanced deep learning models, collaborating with colleagues to deliver impactful solutions for fraud detection, risk management, and identity verification. This position provides the opportunity to contribute to diverse projects and collaborate within a supportive, high-performing team.

Key Responsibilities

• 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.

Qualifications

• Bachelor’s degree with substantial related experience, Master’s degree with relevant experience, or equivalent work background in Computer Science, Statistics, Mathematics, Engineering, or a related field.
• 2-4 years of hands-on experience developing and deploying deep learning models (such as transformers, CNNs, and LSTMs).
• Experience working with diverse data modalities, such as tabular data, text/language, point clouds, and images.
• Proficiency in Python and major ML libraries/frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
• Foundational 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)
• Experience with LLMs and Agentic AI framework/infrastructure (e.g., LangChain/LangGraph/Ray) is a plus.
• Strong problem-solving skills, with the ability to work independently on straightforward tasks and contribute effectively to project objectives.
• Demonstrated ability to collaborate in a diverse, cross-functional team environment.
• Excellent written and verbal communication skills.
• Experience in fraud prevention, risk modeling, or identity verification is a plus.