Job Description

Join the Fraud Data Science team at JPMC and help drive innovation in fraud identification and prevention. As part of our team, you will design, develop, and deploy cutting-edge AI/ML solutions-including graph analytics and Large Language Models (LLMs)-to tackle complex fraud challenges and deliver measurable business impact.

Key Responsibilities:

Develop and implement advanced machine learning models (supervised and unsupervised) for fraud detection and prevention.
Build and maintain graph analytics solutions to uncover fraud patterns and relationships.
Leverage big data and cloud platforms (e.g., AWS, Spark) to automate, scale, and productionalize analytical models/ AI ML tools.
Collaborate with cross-functional teams to translate business needs into actionable data science solutions.
Present insights and recommendations to stakeholders, clearly communicating technical results and business impact.
Document processes and ensure governance compliance for all analytical solutions.

Required Qualifications:

Develop and implement advanced machine learning models (supervised and unsupervised) for fraud detection and prevention.
Hands-on experience with supervised and unsupervised machine learning statistical models. Knowledge of Graph Analytics is a big plus.
Experience with Large Language Models (LLM) and Agentic AI will be an added advantage although not mandatory.
Strong technical skills in Python, PySpark, SQL, and big data/cloud platforms.
Excellent problem-solving and communication skills. Ability to communicate complex findings clearly in both written format and verbally to technical and non-technical audiences.
5+ years of experience with Bachelor's or Master's in a quantitative field (Mathematics, Statistics, Economics, Computer Science, Engineering, etc.).

Good to Haves:

Experience in developing and deploying production-quality machine learning models.
Familiarity with dashboarding tools (e.g., Tableau) and cloud services (AWS Sagemaker), Databricks etc.

CCB Risk Modeling - Sr Data Scientist Associate Fraud Risk