Senior Fraud Risk ML Engineer

JPMorgan Chase & Co.; Plano (AL); On-site; USD 110,000 - 150,000; Full time

Job summary

JPMorgan Chase & Co. is seeking a Risk program Senior Associate in the Chase Consumer Bank to lead ML initiatives for fraud risk ranking across transactions and new products. Responsibilities include feature engineering, model development, and collaboration with business teams to deploy predictive patterns on large-scale datasets.

The role emphasizes expertise in ML techniques, big data platforms, and practical model design to reduce fraud loss and enhance customer experience.

Qualifications

• Master's degree in Mathematics, Statistics, Economics, Computer Science, Operations Research, Physics, or a related quantitative field.
• 2+ years of experience with data analysis in Python.
• Experience designing models for commercial use using at least 3 techniques (CNN, RNN, SVM, RL, RF/GBM).
• A strong interest in how models work and practical aspects of design.

Responsibilities

• Identify and retool ML algorithms to analyze datasets for fraud detection in the Chase Consumer Bank.
• Perform ML tasks such as feature engineering, feature selection, and training models using cutting-edge tech to extract predictive patterns from billions of records.
• Collaborate with business teams to identify opportunities and provide guidance on ML solutions.
• Interact with a broader firm audience to share knowledge, disseminate findings, and provide domain expertise.

Skills

Python

Machine learning

Feature engineering

Data analysis

Education

Master's degree in Mathematics/Statistics/Economics/CS/Operations Research/Physics

PhD in quantitative field with ML publications

Tools

Hadoop

Spark

Hive

Keras/TensorFlow

Job description

JPMorgan Chase & Co. is seeking a Risk program Senior Associate in the Chase Consumer Bank to lead ML initiatives for fraud risk ranking across transactions and new products. Responsibilities include feature engineering, model development, and collaboration with business teams to deploy predictive patterns on large-scale datasets.

The role emphasizes expertise in ML techniques, big data platforms, and practical model design to reduce fraud loss and enhance customer experience.