Director, Autonomous Ai and Analytics

**About the Role**:
**Grade Level (for internal use)**: 13
**About the Role**:The Team**:
The Director of Autonomous AI & Analytics Orchestration is responsible for architecting and operationalizing the next generation of intelligent, autonomous agents across the organization. This role integrates multi-modal data, real-time market signals, and advanced modeling outputs to deliver continuous, self-updating insights that scale decision quality and operational efficiency. The Director provides both strategic and technical leadership, ensuring autonomous workflows become a core capability embedded across commercial, analytical, and operational functions.
**Responsibilities and Impact**:
Lead enterprise-wide adoption of AI-driven analytics and autonomous agent systems, transforming decision-making processes across the Energy division while establishing the technical foundation for scalable, self-improving analytical workflows that adapt to changing market conditions
Architect and deploy sophisticated agentic systems that seamlessly integrate multi-modal data sources, real-time market signals, and advanced modelling outputs to deliver continuous, self-updating insights at enterprise scale, enabling proactive rather than reactive business strategies
Drive strategic alignment of autonomous workflow programs with commercial strategy and product roadmaps, ensuring automated insights directly support revenue growth, operational efficiency, and competitive advantage while maintaining clear ROI metrics and business value demonstration
Establish comprehensive technical governance frameworks for autonomous systems, including robust auditability mechanisms, regulatory compliance protocols, security standards, and ethical AI practices that meet both corporate requirements and evolving industry regulations
Scope, design, and deliver complex end-to-end autonomous analytics initiatives from initial exploration through production deployment, with measurable business impact including significant cycle-time reduction, enhanced decision quality, and operational cost optimization across multiple business units
Collaborate extensively with cross-functional teams, external technology partners, and academic institutions to advance cutting-edge capabilities in real-time data integration, knowledge-graph enrichment, autonomous agent design, and emerging AI technologies that position the organization as an industry leader
**What W e’re L ooking F or**:Basic Required Qualifications**:
Advanced degree in Computer Science, Engineering, or related technical field, with 8+ years of progressive experience leading AI/ML architecture and strategy in enterprise environments, including 3+ years in senior technical leadership roles
Proven expertise in designing and implementing enterprise-scale AI/ML architectures using cloud platforms such as AWS, Azure, or Google Cloud Platform, with deep understanding of distributed systems, microservices, and scalable data infrastructure design patterns
Strong technical leadership experience managing cross-functional engineering teams and driving architectural decisions for complex AI systems, with demonstrated ability to mentor senior developers and establish technical standards across multiple projects
Extensive experience architecting data ecosystems and analytics platforms using technologies such as Snowflake, Databricks, or similar enterprise data platforms, with a focus on system integration, performance optimization, and enterprise security frameworks
Demonstrated success in translating business strategy into technical roadmaps and leading large-scale digital transformation initiatives, with proven ability to collaborate with C-level executives and drive consensus across multiple stakeholder groups
Strong foundation in programming languages such as Python, Java, or Scala, with hands-on experience in AI/ML frameworks like TensorFlow or PyTorch to effectively guide technical teams and validate architectural approaches
**Additional Preferred Qualifications**:
Experience leading AI governance frameworks and establishing MLOps practices across enterprise organizations, including model lifecycle management, compliance protocols, and risk assessment methodologies
Track record of driving innovation through emerging technologies such as generative AI, large language models, or advanced analytics platforms, with experience evaluating and implementing cutting-edge solutions in production environments
Demonstrated expertise in building strategic technology partnerships and vendor relationships, with experience in contract negotiation, technology evaluation, and managing complex multi-vendor AI solution implementations
**Right to Work Requirements**:
This role is limited to persons with an indefinite right to work in the United States and or Canada.
About S&P Global Commodity Insights
At S&P Global Commodity Insights, our complete view of global energy and commodities markets enables our customers to make decisions