Within Chase Community Bank, AI and machine learning are at the heart of how tens of millions of customers experience banking every day — from personalized recommendations and intelligent servicing to fraud detection and digital automation. Our Applied AI/ML team builds, deploys, and governs the models that power this experience, and we are expanding rapidly as we integrate the latest advances in generative AI into our platforms. If you are passionate about driving real-world impact through AI at scale, this is the team for you.
As an Applied AI ML Vice President within Chase Community Bank’s Digital & Technology organization, you will work with a team of data scientists and ML engineers to drive the development, monitoring, and governance of production AI/ML models — including large language models and generative AI systems — that drive the Chase digital experience. You will serve as a senior technical and strategic voice, partnering across lines of business, model risk, compliance, and product teams to ensure our AI ecosystem is innovative, responsible, and continuously improving.
Job Responsibilities
- Establish and maintain robust model governance frameworks in alignment with firm-wide model risk management policies and regulatory requirements, ensuring all AI/ML models adhere to established monitoring, validation, and control standards.
- Partner with model risk management, compliance, and audit teams to oversee model documentation, periodic review cycles, and risk tiering for AI/ML and generative AI assets.
- Lead the monitoring and performance management of AI/ML models in production, including predictive models and generative AI systems, ensuring accuracy, fairness, and stability across the Chase digital platform.
- Identify and implement opportunities to leverage generative AI tools — including LLM-based automation, agentic workflows, and prompt engineering — to accelerate team productivity, improve model governance pipeline, and enhance operational efficiency.
- Communicate model performance, risk posture, and AI innovation initiatives to senior leadership, translating complex technical concepts for non-technical stakeholders.
Required qualifications, capabilities, and skills:
- Undergraduate degree with 4+ years OR master’s degree with 3+ in Computer Science, with training and work experience in applied machine learning, data science, or AI engineering,
- Deep expertise in the full ML model lifecycle — including development, deployment, monitoring, and governance — with demonstrated experience managing production models at enterprise scale.
- Proven ability to lead cross-functional initiatives and influence senior stakeholders in a matrixed organization.
- Excellent written and verbal communication skills, with the ability to present technical findings and risk summaries to executive audiences.
Preferred qualifications, capabilities, and skills:
- Hands-on experience with generative AI technologies, including large language models (LLMs), retrieval-augmented generation (RAG), prompt engineering, and agentic frameworks.
- Experience in consumer banking, digital financial services, or a highly regulated industry.
- Track record of leveraging generative AI to drive automation and efficiency improvements within a data science or technology organization.