CCB Risk Program Associate

JPMorgan Chase & Co.•Wilmington, DE

6d•$135,000 - $165,000

About The Position

This role focuses on designing and developing machine learning models to drive impactful fraud modeling across the entire customer lifecycle, including acquisition, account management, transaction authorization, and collections. The associate will apply state-of-the-art machine learning methodologies, including deep learning, transformer-based models, and LLMs, on big data platforms to solve complex business challenges. A key aspect of the role involves strategic collaboration with senior management for innovative modeling solutions and their deployment, as well as cross-functional partnership with risk, technology, model governance, and research teams throughout the modeling lifecycle.

Requirements

Ph.D. or Master’s degree from a reputable institution in a quantitative discipline such as Computer Science, Mathematics, Statistics, Econometrics, or Engineering.
5+ years' experience in creating predictive models, and generative AI solutions using LLM prompt engineering.
Hands-on experience with LLM APIs, Python libraries like Pandas, NumPy, scikit-learn, and others for data manipulation, modeling and analysis.
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.
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.
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.
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.

Nice To Haves

Strong expertise, interest, and track record of performing cutting-edge research on Gen-AI
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.
Demonstrated expertise in data wrangling and model building on a distributed Cloud computation environment (with stability, scalability and efficiency). GPU experience is desired.
Strong ownership and execution; proven experience in implementing models in production.

Responsibilities

Design and develop machine learning models to drive impactful fraud modeling, covering the entire customer lifecycle, including acquisition, account management, transaction authorization, and collections.
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.
Work closely with senior management to develop and implement ambitious, innovative modeling solutions, ensuring their successful deployment into production environments.
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.