Risk Management & Compliance - Data Scientist Director

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 challenging the status quo and striving to be best in class.

As a Data Scientist Director in the Commercial and Investment Bank (CIB) Risk Management & compliance team, you will be supporting Market Risk and Country Risk, and have the opportunity to 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.

• Oversees and manages a team of data scientists who are responsible for the development of predictive models, autonomous agents, and prompt-based LLM solutions in collaboration with Engineering teams.
• Manages the end-to-end model development lifecycle, including planning, execution, continuous improvement, risk management, and ensuring solutions are scalable and aligned with business objectives.
• Collaborates 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.
• Designs, builds, and deploys impactful AI and data-driven applications using 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.
• Integrates advanced analytics models and applications into operational workflows to ensure business value and adoption.
• Guides research initiatives and pilot projects to identify and apply cutting-edge AI/ML solutions, including GenAI and agentic technologies.
• Implements robust drift monitoring and model retraining processes to maintain accuracy and performance (ongoing performance monitoring).
• Communicates analytical findings and recommendations to senior leadership.

• 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., Snowflake, Databricks), 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.
• Proven experience in agentic frameworks (using CruxAI, Google ADK, LangGraph).

• Advanced degree (Master's or Ph.D.) in Data Science, Computer Science, Mathematics, Engineering, or a related field is preferred.