Senior Quant Analytics Associate - Fraud Risk

Job Responsibilities

• Analyze large datasets to detect patterns, trends, and anomalies indicative of fraudulent activity.
• Build, develop, and maintain reporting and data automation systems to communicate insights to leadership for strategic decision-making.
• Enhance internal analytical techniques and introduce best practices to improve key business metrics.
• Collaborate with cross-functional partners, from problem identification to data analysis and delivering actionable recommendations.
• Develop and implement GenAI and Agentic AI solutions using Python to automate and optimize decision-making processes.
• Apply large language models (LLMs), machine learning (ML) techniques, and statistical analysis to improve decision-making and workflow efficiency across fraud operations and customer experience.
• Design and demonstrate proof-of-concepts (POCs) for extracting insights from structured and unstructured data using advanced analytics; build and iterate on prototype solutions.
• Stay current with the latest research in LLM, ML, and data science, and leverage emerging techniques for ongoing enhancement.

Required Qualifications, Capabilities, And Skills

• Advanced degree in a quantitative discipline (e.g., Computer Science, Mathematics, Operations Research, Data Science).
• 3+ years of experience in Risk Management or any quantitative field.
• Hands-on experience with SQL, Python, and Alteryx.
• Strong understanding of the foundational principles and practical implementation of machine learning algorithms for anomaly detection, including clustering, classification, neural networks, distance-based, and time series methods.
• Experience creating generative AI solutions using LLM prompt engineering and Retrieval Augmented Generation (RAG).
• Experience with evaluation metrics for ML and generative AI.
• Demonstrated ability to communicate complex concepts and results to both technical and business audiences.

Preferred Qualifications, Capabilities, And Skills

• Hands-on experience with behavioral and transactional analytics tools and techniques.
• Familiarity with model explain ability and self-validation techniques.
• Preferred experience supporting more than one CCB Operations Function/Line of Business.