CCB Risk Modeling - AI ML Sr. Associate

JPMorgan Chase & Co.•Palo Alto, CA

About The Position

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.

Requirements

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).

Nice To Haves

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.

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.