Product Manager, Machine Learning Lifecycle

The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and AI journey across

Asset & Wealth Management, Consumer & Community Banking, Corporate & Investment Bank, and Corporate function lines of business. CDAO is at the forefront of artificial intelligence innovation, supporting a vast community of data and AI practitioners. CDAO is responsible for developing and implementing solutions that support the firm’s commercial goals by harnessing AI and ML technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly.

Within CDAO, the Machine Learning Product Management team focuses on building solutions that accelerate the model development life cycle, scale deployment lifecycle, and integrate with AI Apps. The team owns the central platform and infrastructure services for the end-to-end ML workflow, including feature exploration, model prototyping, AI app visualization, and model deployment.

As a ML product manager within the Machine Learning Product Management team, you will be responsible for understanding client needs, designing solutions aligned with the team’s strategic vision, and overseeing the strategy and delivery for the product. You will work closely with cross-functional teams to deliver best-in-class products and capabilities for the firm. As a Platform, we enable our users to more quickly and more efficiently build and deploy models, with enterprise scale, reliability, and governance.

• Define a strategic vision and roadmap for the platform that includes milestones, deliverables, resources, and timelines. Translate user and business needs into strategy and actionable requirements.
• Drive the development of platform capabilities for streamlining the passage of models through the Model Development Lifecycle (MLOps), with the objective of reducing the time to value
• Work with stakeholders across the various businesses (Investment Bank, Consumer Bank, etc.) and functional groups (Legal, Technology, Controls) to collect business requirements, create PRDs, and ship high quality products that solve business needs
• Collaborate across business areas including engineering, data science, client engagement, architecture, and UX teams to deliver impactful business outcomes
• Seamlessly integrate the Dev Ops experience with the MLOps experience and
• Drive resource efficiency of centrally shared AI infrastructure and services
• Drive the development of platform capabilities for streamlining data connectivity and consumption to AI/ML services at enterprise scale for model training and serving.
• Own maintains, and develops a product backlog that enables development to support the overall strategic roadmap and value proposition
• Build the framework and tracks the product's key success metrics
• Drive adoption and create awareness of firmwide solutions through clear and concise messaging about the platform's capabilities and benefits
• Communicate execution strategy to stakeholders, clients, and senior leaders through a well-defined roadmap and Identifies how AI can impact top-line business metrics.

• 5+ years of experience or equivalent expertise in product management or a relevant domain area
• Strong background in advanced analytics, machine learning, traditional AI, or generative AI applications in a business context
• Experience with the unique challenges of deploying and maintaining AI models and expertise in Kubernetes, Containers, CI/CD, and Dev Ops
• Expertise in Cloud computing architectures, including experience with cloud-based data platforms and technologies, such as Amazon Web Services, Microsoft Azure, and Google Cloud
• Expertise in…