CCB Risk Modeling Senior Associate, Fraud Prevention

JPMorgan Chase

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Wilmington, DE
Employment: Full-time
Posted: 15 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $188k

$138k $238k

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This listing doesn't post a salary. Most similar roles pay $147,500–$228,734.

Based on 240 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1226 open roles on FindRole.

Listed pay typically runs $156,750–$215,000 across 5 roles with salary data.

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At a glance

TL;DR · CCB Risk Modeling Senior Associate, Fraud Prevention

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As a CCB Risk Modeling Senior Associate - Fraud Prevention, you will serve as a hands-on individual contributor helping to shape technical direction and build long-term firmwide capabilities to identify and prevent fraud. You will develop, train, and deploy machine learning models, research novel architectures like Graph Networks and Agentic AI, and build AI agents. Your daily work involves using Python, SQL, PySpark, PyTorch, TensorFlow, XGBoost, and Scikit-learn to create data pipelines and dashboards within an AWS environment using Databricks. You will monitor model performance against evolving fraud patterns and mentor junior team members. This role requires applying deep learning theory and classical machine learning to large datasets to solve complex risk management problems and ensure the reliability of automated decisions on live financial transactions.

Skills

Python SQL PySpark AWS Databricks PyTorch TensorFlow XGBoost Scikit-learn Large Language Models Agentic AI Graph Networks GSQL Machine Learning Deep Learning Data Pipelines

What you'll do

Develop, train, and deploy machine learning models for fraud prevention and risk management.
Research and implement novel architectures including Graph Networks, Agentic AI, and Large Language Models.
Build, test, and iterate on AI agents to enhance functionality and user experience.
Create data pipelines and dashboards using Databricks and PySpark to support AI-driven insights.
Monitor and optimize model performance in real-world environments to adapt to evolving fraud patterns.
Lead technical strategy and guide the analytical direction for the team's fraud prevention capabilities.
Develop scalable, reusable machine learning solutions to strengthen firmwide fraud prevention.

What we're looking for

Master’s degree in Computer Science, Mathematics, Statistics, Economics, or a related quantitative field, or equivalent work experience.
Minimum 3 years of experience developing and managing predictive risk models in financial institutions.
Deep understanding of machine learning theory and algorithms with hands-on experience in classical and deep learning methods.
Proficiency in Python, SQL, or PySpark with experience in PyTorch, TensorFlow, XGBoost, or Scikit-learn.
Experience building data pipelines and working with large datasets using Databricks, PySpark, or similar technologies.
Experience working in AWS cloud environments.
Experience mentoring or coaching junior team members.
Knowledge of graph analytics including GSQL (preferred).

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