Applied AI/ML Associate
Company: JPMorganChase
Location: Palo Alto
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. Experience in designing
models for a commercial purpose using some (at least 3) of the
following machine learning and optimization techniques: deep
learning, Natural Language Processing (NLP), graph algorithms, GNN,
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
development in a cloud environment such as AWS SageMaker, GCP
Vertex AI or Azure Machine Learning. Experience of developing
models with Keras/Pytorch/TensorFlow on GPU-accelerated
hardware.
Keywords: JPMorganChase, Novato , Applied AI/ML Associate, Science, Research & Development , Palo Alto, California
