CCB Risk Modeling - AI ML Senior Associate
Company: JPMorganChase
Location: Palo Alto
Posted on: April 1, 2026
|
|
|
Job Description:
Description The CCB Risk Modeling team is seeking talented
individuals with expertise in machine learning, big data, and
distributed computing for applications in credit and fraud
modeling. The team focuses on building AI agents and GenAI
solutions that power next-generation AI capabilities, with a
mission to rapidly build, evaluate, and deploy high-performance AI
agents with production-grade infrastructure, robust evaluation,
observability, and continuous optimization. Job Responsibilities:
Model Development: Design and develop machine learning models to
support impactful decisions across credit and fraud modeling,
covering the entire customer lifecycle, including acquisition,
account management, transaction authorization, and collections.
AI/ML Tools and Frameworks: Research, develop, document, implement,
maintain, and support tools and frameworks that enhance AI/ML model
explainability and fairness, ensuring transparency and ethical use
of models. Advanced Machine Learning Techniques: Utilize
state-of-the-art machine learning methodologies and construct
sophisticated models, including deep learning architectures, on big
data platforms to solve complex business challenges. Agentic AI
Systems: Design and implement tool-calling agents combining
retrieval, structured reasoning, and secure action execution with
robust guardrails for safety and compliance. RAG Pipeline
Development: Curate domain knowledge, build data-quality validation
frameworks, and establish feedback loops to maintain knowledge
freshness. Cross-Functional Partnership: Collaborate with diverse
teams, including marketing, risk, technology, model governance, and
research, throughout the entire modeling lifecycle—from development
and review to deployment and operational use. Required
Qualifications, Capabilities and Skills: Master’s degree in
mathematics, Statistics, Economics, Computer Science, Operations
Research, Physics, or related quantitative fields. 2 years of
experience with data analysis in Python. Proven track record
designing, building, and deploying high-quality machine learning
models in production environments. In-depth knowledge of advanced
ML algorithms: regressions, XGBoost, Deep Neural Networks
(CNN/RNN), clustering, and recommendation systems. Experience
interpreting complex models (XGBoost, GBM, deep learning).
Familiarity with large language models, including fine-tuning and
deployment for NLP tasks. Minimum one year of hands-on experience
with Python, TensorFlow, Spark, or Scala, and big data technologies
(Hadoop, Teradata, AWS Cloud, Hive). Preferred Qualifications,
Capabilities and Skills: PhD in a quantitative field with
publications in top journals, preferably in machine learning.
Strong expertise and research track record in Explainable AI (XAI)
and LLMs. Expertise in data wrangling and model building on
distributed Spark environments with stability, scalability, and
efficiency. GPU experiences desired. Hands-on experience with LLM
techniques: prompt engineering, fine-tuning, model distillation,
and optimization (DPO, PPO). Experience building agentic AI
systems: tool-calling agents with retrieval, reasoning, secure
execution (function calling, orchestration, policy enforcement)
following MCP protocol, including safety and compliance guardrails.
Experience building RAG pipelines: domain knowledge curation,
data-quality validation, and feedback loops for knowledge
freshness. Proven production implementation track record with
strong ownership and execution.
Keywords: JPMorganChase, Gilroy , CCB Risk Modeling - AI ML Senior Associate, Science, Research & Development , Palo Alto, California
