CCB Risk Program Associate

Come and join us in reshaping the future

As a Risk program Senior Associate within the Chase consumer Bank, you'll be the analytical expert for identifying and retooling suitable machine learning algorithms that can enhance the fraud risk ranking of particular transactions and/or applications for new products. This includes a balance of feature engineering, feature selection, and developing and training machine learning algorithms using cutting edge technology to extract predictive models/patterns from data gathered for billions of transactions.

Your expertise and insights will help us effectively utilize big data platforms, data assets, and analytical capabilities to control fraud loss and improve customer experience.

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

• 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 the following machine learning and optimization techniques: CNN, RNN, SVM, Reinforcement Learning, Random Forest/GBM.
• A strong interest in how models work, the reasons why particular models work or not work on particular problems, and the practical aspects of how new models are designed.

• PhD in a quantitative field with publications in top journals, preferably in machine learning.
• Experience with model design in a big data environment making use of distributed/parallel processing via Hadoop, particularly Spark and Hive.
• Experience designing models with Keras/Tensor Flow on GPU‑accelerated hardware.
• Experience with graph technology, including designing and implementing graph‑based machine learning models for fraud detection or risk assessment. Familiarity with graph databases (such as Tiger Graph or Neo4j…), graph algorithms (e.g., node classification, link prediction, community detection), and graph feature engineering is highly desirable. Ability to leverage graph analytics to uncover complex relationships and patterns within large‑scale transaction data is a strong plus.
• Hands‑on experience with transformer models and related architectures (such as BERT, GPT, or Graph Transformers) for natural language processing, anomaly detection, or transaction analysis. Proficiency in fine‑tuning and deploying transformer‑based models using frameworks like PyTorch or Tensor Flow is preferred. Demonstrated ability to apply transformer models to extract meaningful insights from unstructured or semi‑structured data sources will be highly valued.