Senior Quant Analytics Associate - Fraud Risk

JPMorgan Chase & Co.•Wilmington, DE

16h•Onsite

About The Position

If you are passionate about leveraging advanced analytics and AI to combat fraud and drive business value, we encourage you to apply! As a Senior Quantitative Analytics Associate in our Fraud Risk team, you will help prevent plastics fraud through advanced, data-driven analysis. You’ll gain a comprehensive understanding of the point-of-sale transaction lifecycle and deliver timely, efficient, and tailored solutions. You will collaborate with cross-business partners to leverage advanced analytics for fraud/scam prevention, dispute and claim management, and optimization of risk/reward tradeoffs (losses/OpEx/customer experience), with the goal of driving positive business outcomes.

Requirements

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.

Nice To Haves

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.

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.
Work independently and collaboratively 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.