Applied AI ML Director, Risk Management & Compliance

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-word challenges that impact our company, customers and communities. Our culture in Risk Management and Compliance is all about challenging the status quo and striving to be best in class.
As an Applied AI ML Director, Risk Management & Compliance at JPMorganChase within the Digital Strategy and Enablement team, you will be supporting Market Risk, Country Risk and Principal Risk, and you'll shape how we leverage artificial intelligence and advanced analytics to solve complex business challenges. You will guide us in exploring, piloting, and implementing transformative AI solutions, including Gen AI, while collaborating closely with Product, Engineering, and Lines of Business. Together, we will foster a culture of experimentation, delivery, and continuous learning, encouraging new ideas and maintaining operational excellence.
You will help build and mentor a high-performing team focused on deep data understanding and advanced analytics, driving the development of robust tools and solutions using cutting-edge AI/ML techniques, cloud technologies, and enterprise knowledge bases. In this strategic and hands-on role, you will engage with technical aspects, review code, monitor the impact of production GenAI models, and enable rapid prototyping, ensuring our solutions deliver real business value and are seamlessly integrated into our operations.
Job Responsibilities
Oversee and manage a team of data scientists responsible for developing predictive models, prompt-based LLM solutions, autonomous agents and agentic systems.
Lead the design, build, and deployment of impactful AI and data-driven applications in collaboration with Product, Business and Engineering teams. Use cloud, data mesh, and knowledge base technologies such as centralized repositories, semantic search, and automated information retrieval systems that organize, store, and provide easy access to critical business data and insights.
Distill and communicate complex analytical findings and recommendations to senior leadership to inform strategic decisions.
Manage the end-to-end model development lifecycle, including planning, execution, continuous improvement, risk management, and alignment with business objectives.
Adhere to model risk management, responsible AI, and governance standards, including explainability, fairness, and compliance requirements.
Collaborate with senior leaders to re-engineer processes and define a compelling vision for the target state by embedding AI into current workflows, driving change and efficiency.
Integrate data science solutions and applications into operational workflows to drive business value and adoption.
Guide research initiatives and pilot projects to identify and apply state-of-the-art AI/ML solutions, including GenAI and agentic technologies.
Implement robust drift monitoring and model retraining processes to maintain accuracy and performance.
Required Qualifications, Capabilities, and Skills
Extensive experience in data science, analytics or a related field.
Proven track record of deploying, operationalizing, and managing AI, ML, and advanced analytics models in a large-scale enterprise environment, including hands-on experience with ML Ops frameworks, tools, and best practices for model monitoring, automation, and lifecycle management.
Significant leadership experience in managing data science/R&D teams and driving technology innovation.
Extensive experience in AI/ML algorithms, statistical modeling, and scalable data processing pipelines, with a strong background in modern data platforms (e.g. Databricks or equivalent), cloud-based technologies, data mesh architectures, and big data ecosystems.
Experience with A/B experimentation, data- and metric-driven product development, cloud-native deployment in large-scale distributed environments, and the ability to develop and debug production-quality code.
Strong written and verbal communication skills, with the ability to convey technical concepts and results to both technical and business audiences.
Scientific mindset with the ability to innovate and work both independently and collaboratively within a team.
Ability to thrive in a matrix environment and build partnerships with colleagues at various levels and across multiple locations.
Hands-on experience with agentic frameworks and orchestration (e.g., LangGraph, Google ADK or equivalent).
Preferred Qualifications, Capabilities, and Skills
Advanced degree (Master’s or Ph.D.) in Data Science, Computer Science, Mathematics, Engineering, or a related field.