Senior Fraud Risk ML Analyst

JPMorganChase; Plano (TX); On-site; USD 110,000 - 160,000; Full time

Job summary

Chase is seeking a Risk program Senior Associate in the Consumer Bank to identify and retool machine learning algorithms that improve fraud risk ranking for transactions and applications. You will balance feature engineering, feature selection, and model development using big data to reduce fraud loss and enhance customer experience.

You will work with business teams to align ML solutions with strategic goals, explore data assets, and share insights across the firm, leveraging Spark, Hive, and

Qualifications

• Master's degree in Mathematics, Statistics, Economics, CS, OR, Physics or related quantitative field.
• 2+ years of experience with data analysis in Python.
• Experience designing models using CNN, RNN, SVM, RL, RF/GBM.
• Strong interest in model behavior and practical design considerations.

Responsibilities

• Identify and retool ML algorithms to analyze datasets for fraud detection in Chase Consumer Bank.
• Perform feature engineering, feature selection, and training ML models using cutting-edge tech to extract patterns from billions of transactions.
• Collaborate with business teams to identify opportunities and provide guidance on leveraging ML solutions.
• Share knowledge and provide domain expertise across the firm.

Skills

Python

ML modeling

Education

Master's degree in quantitative field

Tools

Hadoop

Spark

Hive

Keras/TensorFlow

Job description

Chase is seeking a Risk program Senior Associate in the Consumer Bank to identify and retool machine learning algorithms that improve fraud risk ranking for transactions and applications. You will balance feature engineering, feature selection, and model development using big data to reduce fraud loss and enhance customer experience.

You will work with business teams to align ML solutions with strategic goals, explore data assets, and share insights across the firm, leveraging Spark, Hive, and