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$139,100–$211,200.
Based on 239 similar postings.
Employer
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
1117 open roles
on FindRole.
Listed pay typically runs
$186,160–$215,000
across 7 roles with salary data.
The CCB Risk Program Associate joins the CCB Risk Modeling team to develop vision and multimodal AI systems focused on fraud modeling applications across the customer lifecycle. This role involves building machine learning models for acquisition, account management, transaction authorization, and collections while developing tools for model evaluation and monitoring. The successful candidate will utilize advanced techniques including deep learning architectures, transformer-based models, and LLMs to solve complex business challenges. Key technical requirements include proficiency in Python, TensorFlow, PyTorch, Spark, or Scala, along with experience in big data platforms like Hadoop, AWS, and Hive. Candidates must possess expertise in computer vision, generative AI, prompt engineering, and model interpretability. The work involves creating robust solutions for visual anomaly detection and synthetic data generation while ensuring alignment with governance, risk, and regulatory expectations.
Develop machine learning models to detect fraud across the entire customer lifecycle.
Build vision and multimodal AI systems for image understanding and visual anomaly detection.
Implement generative AI solutions, including synthetic data generation and LLM prompt engineering.
Apply transformer-based models and deep learning architectures on big data platforms.
Design and deploy scalable machine learning models into production environments.
Develop tools for model evaluation, monitoring, and drift detection.
Ensure all AI/ML solutions comply with governance, risk, and regulatory requirements.
What we're looking for
Ph.D. or Master’s degree in a quantitative discipline such as Computer Science, Mathematics, Statistics, Econometrics, or Engineering.
5+ years of experience creating predictive models and generative AI solutions using LLM prompt engineering.
Hands-on experience with Python libraries including Pandas, NumPy, and scikit-learn for data manipulation and modeling.
In-depth knowledge of advanced machine learning algorithms, including logistic regression, XGBoost, Deep Neural Networks (CNN/RNN), and recommendation systems.
Proficiency in TensorFlow, PyTorch, Spark, or Scala, along with experience in big data technologies like Hadoop, AWS, and Hive.
Familiarity with MLOps tooling for model monitoring, drift detection, and end-to-end auditability.
Experience in model interpretability and explainability for complex models (preferred).
Proven track record of performing cutting-edge research on Gen-AI (preferred).
AI/ML
LLM
Generative AI
Python
R
Neural Networks
Regression
Reinforcement Learning
Recommender Systems
Transformers
Prompt Engineering
RAG
Agentic AI
Statistics
Econometrics
Model Validation
Risk Management
Model Governance