CCB Risk Program Associate
Company: JPMorganChase
Location: Plano
Posted on: April 1, 2026
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Job Description:
Description 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. Job Responsibilities: 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 Required
qualifications, capabilities and skills: 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 some (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. Preferred qualifications, capabilities
and skills: 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/TensorFlow 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 TigerGraph 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 TensorFlow is preferred. Demonstrated ability to
apply transformer models to extract meaningful insights from
unstructured or semi-structured data sources will be highly
valued.
Keywords: JPMorganChase, Plano , CCB Risk Program Associate, Science, Research & Development , Plano, Texas
