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.
- 3+ years of experience with Bachelor's or Master's in a quantitative field (Mathematics, Statistics, Economics, Computer Science, Engineering, etc.).
Required Qualifications:
- Experience developing and deploying production-quality machine learning models.
- Familiarity with dashboarding tools (e.g., Tableau) and cloud services (AWS Sagemaker, Amazon EMR).
