The Portfolio Risk Modeling team within CCB Risk Modeling group is responsible for end-to-end development of best in class forecasting model suite for Chase credit card portfolios to support stress testing, loss reserve, and business planning exercises.

In this role, you, together with a team of highly-skilled quantitative professionals, will work on a large and cleanly structured codebase designed for large-scale distributed simulation and forecasting. Your expertise in machine learning, time series forecasting, causal inference and computer science will not only ensure us to deliver sophisticated models that are performant and in compliance with regulatory requirements and/or firm wide model risk policies but also enable our models and system run efficiently by writing effective and maintainable code. Additionally, you as a business professional will collaborate with business partners in loss forecasting, finance and technology, effectively communicate model results, analytical findings, and insights to them and senior leadership team to support business and or technical decisions.

Job Responsibilities:

• Model Development: Design and develop machine learning models, time series forecasting models to support loss and revenue forecasting under regulatory and business framework.
• 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 Loss Forecasting, Finance, Technology, Governance and Review, throughout the entire modeling lifecycle, from development and review to deployment and operational use.

Required Qualifications, Capabilities and Skills:

• Master’s degree in Computer Science, Mathematics, Statistics, Econometrics, Physics, Engineering, 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: logistic regressions, linear 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.

---

Chase is a leading financial services firm, helping nearly half of America’s households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

Equal Opportunity Employer/Disability/Veterans

---

---

The Portfolio Risk Modeling team in CCB Risk Modeling develops end-to-end forecasting models for Chase credit card portfolios to support stress testing, loss reserves, and business planning. In this role, you’ll build and maintain large-scale simulation and forecasting systems using ML/time-series/causal methods, ensure regulatory and model-risk compliance, and partner with stakeholders across loss forecasting, finance, and technology.