Emerging Risk Strategic Analyst - Consumer & Community Bank - Senior Associate

Job Summary Bring your expertise to JPMorgan Chase. As part of Risk Management and Compliance, you are at the center of keeping JPMorgan Chase strong and resilient. You help the firm grow its business in a responsible way by anticipating new and emerging risks, and using your expert judgement to solve real-world challenges that impact our company, customers and communities. Our culture in Risk Management and Compliance is all about thinking outside the box, challenging the status quo and striving to be best-in-class. As a Risk Intelligence & AI Lead in consumer banking Risk Insights, you will own the development and evolution of AI-powered risk intelligence capabilities that monitor consumer and small business health, detect emerging risks, and deliver actionable insights to senior leadership. You will sit at the intersection of data science, Generative AI, and risk strategy — leading the design and deployment of LLM-driven solutions, agentic AI workflows, and advanced analytical tools that automate research, generate executive narratives, and enhance the speed and quality of risk decision-making. This role offers a rare combination of strategic influence and hands-on technical leadership. You will partner directly with Technology, Data, Risk Management, and Governance teams to build scalable, production-grade AI solutions while ensuring full compliance with firm standards for model governance, responsible AI, and controls. You will translate complex macroeconomic, credit, and portfolio trends into clear, compelling communications for senior executives — making you a critical voice in how the consumer bank understands and responds to the world around it. Job Responsibilities: Develop and maintain risk intelligence capabilities that leverage internal and external data sources, large datasets, and analytical tools to monitor consumer and small business health, credit performance, labor market conditions, and macroeconomic developments Lead the end-to-end development of Generative AI and Agentic AI capabilities for CCB Risk Insights, including architecture design, prompt engineering, agent orchestration, and deployment of LLM-powered solutions Design, build, and maintain AI-powered solutions that automate emerging risk detection, narrative generation, executive reporting, research workflows, and conversational analytics across consumer banking Risk Translate complex risk, economic, and portfolio trends into actionable insights, executive-ready communications, and strategic recommendations for senior leadership Evaluate, test, and implement new AI technologies, analytical tools, and platforms — developing scalable, repeatable capabilities that improve efficiency, insight quality, and risk monitoring effectiveness Partner with Technology, Data, Risk Management, and Governance teams to ensure all analytical and AI solutions comply with firm standards for controls, model governance, security, and responsible AI practices Build and refine automated research and reporting pipelines that synthesize structured and unstructured data into timely, high-quality risk intelligence outputs Identify and assess emerging risks across consumer and small business portfolios by integrating alternative data, market signals, and macroeconomic indicators into monitoring frameworks Develop scalable prototypes and proof-of-concept solutions that demonstrate the value of new AI capabilities, driving adoption and stakeholder buy-in across the organization Collaborate cross-functionally with data engineering, product, and business stakeholders to define requirements, prioritize use cases, and ensure AI solutions address real business needs Stay current on advancements in large language models, agentic AI frameworks, and applied AI research, proactively identifying opportunities to enhance Risk Insights capabilities Required Qualifications, Capabilities, and Skills: 3+ years of experience in data science, risk analytics, AI/ML engineering, or a related quantitative discipline within financial services or a similarly complex, regulated industry Bachelor's degree in Computer Science, Data Science, Statistics, Economics, Engineering, or a related quantitative field Experience developing and deploying Generative AI or LLM-based solutions, including prompt engineering, retrieval-augmented generation (RAG), fine-tuning, or agent-based architectures Strong proficiency in Python and SQL, with demonstrated experience working with large-scale structured and unstructured datasets Proven ability to translate complex analytical findings into clear, concise executive communications and actionable recommendations for senior stakeholders Experience building automated data pipelines, reporting workflows, or analytical tools that operate at production scale Solid understanding of consumer credit risk, macroeconomic indicators, labor market dynamics, or portfolio performance analytics Demonstrated experience partnering with technology, governance, and risk management teams to deliver solutions that meet enterprise-level controls, model risk management, and responsible AI standards Strong knowledge of machine learning frameworks and libraries (e.g., PyTorch, TensorFlow, Hugging Face, LangChain, or equivalent) Excellent problem-solving skills with the ability to work independently, manage ambiguity, and drive projects from concept through production deployment Experience with data visualization tools and techniques for communicating risk trends and analytical outputs to non-technical audience Preferred Qualifications, Capabilities, and Skills: Master's degree or PhD in a quantitative discipline such as Computer Science, Machine Learning, Statistics, Economics, or Computational Finance Experience with agentic AI frameworks, multi-agent orchestration, or autonomous workflow design Familiarity with JPMorganChase internal platforms, data infrastructure, and risk management frameworks Experience with cloud-based AI/Machine Learning platforms (e.g., AWS SageMaker, Azure ML, or equivalent enterprise environments) Background in natural language processing (NLP) applied to financial documents, earnings reports, regulatory filings, or news analytics