Fraud Risk ML Specialist – Senior Associate

Testing Site - JPMC; Plano (TX); On-site; USD 90,000 - 150,000; Full time

Job summary

Chase is seeking a Risk program Senior Associate to advance fraud risk ranking of transactions and applications for new products. You will identify ML algorithms, perform feature engineering, and train models on vast datasets using modern tech stacks to improve fraud control and customer experience.

The role requires a Master’s in a quantitative field, 2 years in Python analytics, and experience with diverse ML techniques. A strong interest in model behavior and practical design is essential.

Qualifications

• Master's degree in Mathematics, Statistics, Economics, Computer Science, Operations Research, Physics, and other related quantitative fields.
• 2 years of experience with data analysis in Python.
• Some Experience in designing models for a commercial purpose using at least 3 of CNN, RNN, SVM, Reinforcement Learning, Random Forest/GBM.
• A strong interest in how models work and why they succeed or fail, and practical aspects of model design.

Responsibilities

• Identify and retool machine learning (ML) algorithms to analyze datasets for fraud detection in the Chase Consumer Bank.
• Perform ML tasks such as feature engineering, feature selection, and developing and training ML algorithms using cutting-edge technology to extract predictive models/patterns from billions of transactions’ data.
• Collaborate with business teams to identify opportunities, collect business needs, and provide guidance on leveraging the ML solutions.
• Interact with a broader audience in the firm to share knowledge, disseminate findings, and provide domain expertise.

Skills

Python data analysis

ML concepts

Fraud detection

Analytical thinking

Education

Master's degree (Math/Stats/CS/OR/Physics)

PhD in quantitative field

Tools

Spark

Hive

Keras/TensorFlow

Graph databases (Neo4j/Tig​erGraph)

PyTorch

Job description

Chase is seeking a Risk program Senior Associate to advance fraud risk ranking of transactions and applications for new products. You will identify ML algorithms, perform feature engineering, and train models on vast datasets using modern tech stacks to improve fraud control and customer experience.

The role requires a Master’s in a quantitative field, 2 years in Python analytics, and experience with diverse ML techniques. A strong interest in model behavior and practical design is essential.