Fraud ML Architect for Risk Programs

JPMorganChase; Wilmington (DE); On-site; USD 150,000 - 230,000; Full time

Job summary

Chase, a leading financial services firm, seeks an experienced Machine Learning Scientist to design and deploy advanced fraud models affecting acquisition, accounts, transactions, and collections. You will collaborate with risk, technology, and research teams to implement scalable, production-ready solutions.

The role emphasizes deep learning, transformer architectures, LLMs, and responsible deployment with monitoring and auditability, leveraging Python, Spark, and cloud platforms.

Qualifications

• Ph.D. or Master’s degree 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.

Responsibilities

• 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.
• 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.
• Strategic Collaboration: Work closely with senior management to develop and implement ambitious, innovative modeling solutions, ensuring their successful deployment into production environments.
• 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.

Skills

LLM prompt engineering

Python

ML model development

Deep learning

Model interpretability

Prompt engineering for LLMs

Spark/PySpark

TensorFlow/PyTorch

Education

Ph.D. or Master’s in a quantitative field

Tools

Pandas

NumPy

scikit-learn

TensorFlow

PyTorch

Spark

Hadoop

AWS

Hive

MLOps tooling

Job description

Chase, a leading financial services firm, seeks an experienced Machine Learning Scientist to design and deploy advanced fraud models affecting acquisition, accounts, transactions, and collections. You will collaborate with risk, technology, and research teams to implement scalable, production-ready solutions.

The role emphasizes deep learning, transformer architectures, LLMs, and responsible deployment with monitoring and auditability, leveraging Python, Spark, and cloud platforms.