CCB Risk Program Associate

JPMorgan Chase & Co.; Wilmington (DE); On-site; USD 180,000 - 240,000; Full time

Job summary

JPMorgan Chase & Co. in Delaware seeks a CCB Risk Modeling specialist with expertise in computer vision, Generative AI, and image generation for fraud modeling applications.

You will help build vision and multimodal AI systems—image understanding, anomaly detection, synthetic data, and GenAI-enabled workflows. You will lead model development, evaluation, and monitoring tooling, and partner with risk, technology, and governance teams to ensure robust, scalable solutions that meet regulatory

Qualifications

• PhD or Master’s degree in a quantitative discipline such as CS, Math, Stats, Econometrics, or Engineering.
• 5+ years’ experience building predictive models and generative AI solutions using prompt engineering.
• Hands-on with LLM APIs and Python data libraries for manipulation, modeling and analysis.

Responsibilities

• Model development for fraud modeling across customer lifecycle (acquisition, accounts, transactions, collections).
• Apply deep learning, transformers, and LLMs on large data platforms to solve business problems.
• Collaborate with senior management to deploy ambitious modeling solutions into production.
• Partner with risk, governance, and research teams through the full modeling lifecycle.

Skills

Python

LLM prompts

GenAI

Pandas

NumPy

scikit-learn

TensorFlow

PyTorch

Spark

Scala

AWS

Hive

MLOps

Governance

Education

PhD or MSc in quantitative field

Tools

LLM APIs

Hadoop

AWS

Hive

Spark

TensorFlow

PyTorch

scikit-learn

Job description

The CCB Risk Modeling team is seeking talented professionals with expertise in computer vision, Generative AI, and image generation, with a focus on fraud modeling applications. Our work centers on building vision and multimodal AI systems—including image understanding, visual anomaly detection, synthetic data/image generation, and GenAI-enabled workflows—across modern ML platforms and emerging agentic patterns. The ideal candidate will drive these initiatives across model development, evaluation and monitoring tooling, and cross-functional collaboration, ensuring AI/ML solutions are robust, scalable, and aligned with governance, risk, and regulatory expectations.

Key Responsibilities

1. Model Development: Design and develop machine learning models to drive impactful fraud modeling, covering the entire customer lifecycle, including acquisition, account management, transaction authorization, and collections.
2. Advanced Machine Learning Techniques: Apply state-of-the-art machine learning methodologies — including deep learning architecture, transformer-based models, and LLMs — on big data platforms to tackle complex business challenges.
3. Strategic Collaboration: Work closely with senior management to develop and implement ambitious, innovative modeling solutions, ensuring their successful deployment into production environments.
4. Cross-Functional Partnership: Collaborate with diverse teams, including risk, technology, model governance, and research, throughout the entire modeling lifecycle—from development and review to deployment and operational use.

Basic Qualifications

1. Ph.D. or Master’s degree from a reputable institution in a quantitative discipline such as Computer Science, Mathematics, Statistics, Econometrics, or Engineering.
2. 5+ years' experience in creating predictive models, and generative AI solutions using LLM prompt engineering.
3. Hands-on experience with LLM APIs, Python libraries like Pandas, NumPy, scikit-learn, and others for data manipulation, modeling and analysis.
4. In-depth knowledge of advanced machine learning algorithms, including logistic regression, XGBoost, Deep Neural Networks (CNN and RNN), clustering, and recommendation systems, with expertise in model design, hyperparameter tuning, and responsible deployment practices.
5. Demonstrated experience in model interpretability and explainability for complex models such as XGBoost and GBM; experience extending these methods to deep learning architectures (CNNs, RNNs, transformers) is a strong plus.
6. Familiarity with large language models (LLMs) and their applications, including experience in fine-tuning, prompt engineering, and responsible deployment with appropriate safeguards, monitoring, and auditability.
7. Proficiency in Python, TensorFlow, PyTorch, Spark, or Scala, coupled with experience in big data technologies such as Hadoop, AWS, and Hive, and familiarity with MLOps tooling that supports model monitoring, drift detection, and end-to-end auditability.

Preferred Qualifications

1. Strong expertise, interest, and track record of performing cutting-edge research on Gen-AI
2. Proven track record in designing, building, and deploying high-quality machine learning models in production environments, demonstrating a strong ability to translate theoretical concepts into practical applications.
3. Demonstrated expertise in data wrangling and model building on a distributed Cloud computation environment (with stability, scalability and efficiency). GPU experience is desired.
4. Strong ownership and execution; proven experience in implementing models in production.