CCB Risk Program Associate
On-site · Plano, Texas, United States
Job Summary
As a Risk Program Associate in Chase Consumer Bank, you will act as the analytical expert for identifying and retooling machine learning algorithms to enhance fraud risk ranking of transactions and applications for new products. Responsibilities include retooling ML algorithms for fraud detection, performing feature engineering and feature selection, developing and training ML models on large datasets, collaborating with business teams to translate needs into ML solutions, and sharing knowledge across the firm to disseminate findings. The role requires strong quantitative training, hands-on Python data-analysis experience, and practical ML model design across big data environments. Preferred qualifications include a PhD with published ML work, distributed processing with Hadoop/Spark/Hive, experience with Keras/TensorFlow on GPUs, graph-based ML, and transformer-model expertise.
Required Qualifications
- Master's degree in Mathematics, Statistics, Economics, Computer Science, Operations Research, Physics, or related quantitative field
- 2 years of experience with data analysis in Python
- Experience designing ML models for commercial use using techniques: CNN, RNN, SVM, Reinforcement Learning, Random Forest/GBM
Desired Qualifications
- PhD in a quantitative field with publications in top journals, preferably in machine learning
- Experience with model design in a big data environment using distributed/parallel processing via Hadoop, Spark and Hive
- Experience designing models with Keras/TensorFlow on GPU-accelerated hardware
- Experience with graph technology and graph databases (TigerGraph or Neo4j) and graph feature engineering
- Hands-on experience with transformer models (BERT, GPT, Graph Transformers) and frameworks like PyTorch or TensorFlow
- Strong Python data analysis skills (2+ years)
- Experience with ML techniques: CNN, RNN, SVM, Reinforcement Learning, Random Forest/GBM
- Ability to engineer features and understand model behavior across large-scale fraud detection datasets
- Interest in how models work and practical aspects of model design
- Ability to collaborate with business teams and convey findings effectively
- Experience with big data ecosystems and distributed computing
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