CCB Risk Program Associate

JPMorgan Chase & Co.; Wilmington (DE); On-site; USD 140,000 - 190,000; Full time

Job summary

JPMorgan Chase & Co. in Wilmington, DE is seeking a senior ML/AI modeling expert to design and deploy predictive models across customer lifecycle, including acquisition, accounts, transactions and collections.

You will collaborate with risk, technology, and governance teams to translate research into production systems. The role requires advanced ML knowledge, experience with LLMs and scalable data platforms, and a track record of production deployments.

Qualifications

• Ph.D. or Master’s degree in Computer Science, Mathematics, Statistics, Econometrics, or Engineering.
• 5+ years' experience building predictive models and generative AI solutions with LLM prompt engineering.
• Hands-on experience with LLM APIs and Python libraries (Pandas, NumPy, scikit-learn) for data manipulation and modeling.
• Deep knowledge of ML algorithms (logistic regression, XGBoost, DNNs, clustering, recommender systems) and model deployment.
• Experience in model interpretability and explainability for complex models (XGBoost/GBM; deep learning extensions).
• Familiarity with LLMs, fine-tuning, prompt engineering, and responsible deployment with safeguards and auditability.
• Proficiency in Python, TensorFlow, PyTorch, Spark, or Scala, and big data technologies (Hadoop, AWS, Hive); familiarity with MLOps tooling.

Responsibilities

• Design and develop machine learning models to drive fraud modeling across customer lifecycle.
• Apply state-of-the-art ML methods including deep learning and LLMs on big data platforms.
• Collaborate with senior management to deploy innovative modeling solutions into production.
• Work with risk, tech, model governance, and research teams across the modeling lifecycle.

Skills

LLM prompt engineering

Python (Pandas, NumPy)

ML algorithms (XGBoost, DNNs)

Model interpretability

Production deployment

MLOps monitoring

Education

PhD or Master’s in a quantitative field

Tools

Pandas

NumPy

scikit-learn

TensorFlow

PyTorch

Spark

AWS/Hadoop

Job description

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.