Global Head of AI and ML Field Development, Engineering and Data Science, SVP

Who we are looking for

Lead a global AI & ML Field Deployment Engineering and Data Science organization that partners directly with businesses to develop, deploy, and scale AI, machine learning, and data science solutions, delivering on State Street’s enterprise and business objectives.

The Head of AI & ML Field Deployment Engineering & Data Science is accountable for ensuring that State Street’s AI capabilities translate into real, measurable business outcomes across all lines of business.

This role leads a business-aligned engineering and data science organization that works directly with:

• Investment Services
• Investment Management
• Wealth
• Alpha platform
• Global Markets
• Corporate and control functions

to deliver model-driven insights, predictive capabilities, and AI-powered solutions embedded into business workflows.

This is a front-line execution role, focused on:

• Developing and deploying AI/ML and data science solutions
• Scaling adoption of models across business processes
• Enabling effective use of enterprise AI platforms
• Delivering tangible business outcomes through AI

The role serves as the execution bridge between AI platform capabilities and business impact, ensuring that models and analytical solutions are not only built, but productionized, scaled, and delivering value.

Success is measured by AI adoption, model deployment at scale, speed to production, and business outcomes delivered through AI and data science.

What you will do

Business-Aligned AI, ML & Data Science Execution

• Lead a global organization of:
  • Data scientists
  • Machine learning engineers
  • Applied AI engineers
• Partner directly with business and technology teams to:
  • Develop AI/ML and data science solutions aligned to business objectives
  • Deploy models into production environments
  • Scale AI capabilities across business workflows
• Act as a trusted AI and data science partner to business leaders

Delivery of AI-Driven Business Outcomes

• Translate AI, ML, and data science capabilities into:
  • Revenue and client opportunities
  • Operational efficiencies
  • Decision intelligence and insights
• Ensure alignment of solutions with:
  • Enterprise strategy
  • Business-specific KPIs
• Drive measurable impact across all business domains

AI/ML & Data Science Solution Development

• Deliver end-to-end solutions, including:
  • Predictive and machine learning models
  • Statistical and analytical models
  • Feature engineering and data pipelines
  • Generative AI use cases (non-agentic)
• Ensure efficient progression from:
  • Experimentation → production → scaled deployment
• Standardize delivery approaches for repeatability and scale

AI Platform Adoption & Utilization

• Drive adoption of enterprise AI platforms by:
  • Enabling model development and deployment workflows
  • Supporting teams in leveraging platform capabilities effectively
• Ensure consistent usage of platform capabilities across the enterprise

Reusable Models, Features & Analytical Assets

• Promote reuse of:
  • Feature engineering pipelines
  • Model components and frameworks
  • Analytical and statistical methodologies
• Reduce duplication and accelerate time to value across teams

Data Scientist & Engineer Enablement

• Improve productivity of data scientists and AI engineers by:
  • Enabling self-service capabilities
  • Streamlining development and deployment workflows
  • Simplifying access to data and tools
• Establish best practices across:
  • Model lifecycle management
  • Experimentation and evaluation
  • Production deployment

Feedback Loop to Platform & Data Teams

• Serve as the voice of AI practitioners and business users into:
  • AI Platform Engineering
  • Data Platform Engineering
• Identify:
  • Platform usability and capability gaps
  • Data availability and quality issues
  • Scaling challenges in production environments
• Drive continuous improvement based on real-world usage

Standardized AI Delivery Patterns

• Develop and scale repeatable playbooks and patterns for:
  • Model development
  • Deployment and operationalization
  • Scaling across business use cases