Lead I - Autonomous AI & Analytics Orchestration

Role Summary

The Lead 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

Strategic Leadership & Organizational Adoption

• Champion the enterprise-wide adoption of AI-driven analytics, autonomous agents, and self-orchestrating workflows, showcasing their role in transforming decision-making and operational models.
• Align autonomous workflow programs with commercial strategy, product roadmaps, and business priorities to ensure automated insights directly support revenue growth, efficiency, and customer value.
• Set a long-term vision and roadmap for autonomous analytics, agentic systems, and AI-first operational paradigms across the organization.

Agentic System Architecture & Orchestration

• Architect the integration of structured data, unstructured content, real-time signals, and modeling outputs into cohesive pipelines that feed intelligent, autonomous agents.
• Lead the design and deployment of agentic architectures capable of consuming modeling outcomes, dynamic market data, and internal knowledge to generate continuous, explainable, and self-updating insights.
• Establish standards for agent orchestration, including message passing, retrieval augmentation, workflow state management, and agent-to-agent coordination.

Data Engineering, Modeling Integration & Real-Time Systems

• Partner with Data Engineering, Modeling, Cloud Architecture, and Product teams to unify data flows across streaming ingestion, model execution layers, knowledge graphs, and analytics platforms.
• Ensure seamless integration of:
  • Modeling engines (statistical, optimization, ML, physics-based)
  • Real-time market and operational signals
  • Document stores, vector databases, and embeddings
  • Event-driven and microservices-based pipelines
• Drive architecture decisions around latency, throughput, concurrency, and fault tolerance for real-time autonomous systems.

Technical Governance, Reliability & Compliance

• Define and enforce technical standards for agentic systems, including auditability, lineage tracking, reproducibility, and controlled execution.
• Oversee security and compliance frameworks for automated agents, ensuring adherence to corporate governance, access policies, and regulatory requirements.
• Lead operationalization of autonomous workloads, including monitoring, A/B testing, drift detection, and continuous performance validation.

Program Delivery & Scaling

• Scope autonomous agent projects from exploration to production-grade deployment, ensuring clear definition of objectives, KPIs, architecture, resourcing, and success metrics.
• Drive end-to-end delivery of autonomous analytics initiatives, ensuring scalable, maintainable, and resilient implementations.
• Establish frameworks to measure business impact, including cycle-time reduction, efficiency gains, cost avoidance, and decision quality improvements.

Cross-Functional Collaboration & Innovation

• Collaborate with internal SMEs, research teams, and external partners to advance capabilities in real-time data integration, knowledge-graph enrichment, and next-generation autonomous agent design.
• Identify and prioritize high-impact use cases where agentic systems can replace manual processes, enhance insight generation, or create new commercial opportunities.
• Represent the organization in technical forums, conferences, and external collaborations related to AI, autonomous systems, and agentic architectures.

Qualifications

Required

• 10+ years of experience in Data Science, Machine Learning, AI systems, or advanced analytics, with a track record of leading technical teams.
• Deep expertise in distributed systems, real-time data pipelines, and cloud-native architectures (e.g., AWS, Databricks, Kubernetes, event-driven architectures).
• Demonstrated experience architecting and deploying AI/ML systems, including model orchestration, inference pipelines, and autonomous or semi-autonomous workflows.
• Strong understanding of RAG pipelines, LLM-based agentic systems, vector databases, embeddings, and multi-agent frameworks.
• Experience leading large-scale cross-functional programs with measurable business impact.
• Proven ability to communicate complex technical concepts to senior executives and non-technical stakeholders.

Preferred

• Background in optimization, time-series modeling, or domain-specific modeling frameworks.
• Experience with knowledge-graph technologies and semantic search systems.
• Familiarity with reinforcement learning, decision automation engines, or multi-agent ecosystems.
• Prior leadership experience in energy, financial services, or other data-intensive industries.

Why This Role Matters

The Lead of Autonomous AI & Analytics Orchestration lays the foundation for the organizations transition toward intelligent, self-operating analytical systems. This role will define how data, models, and AI agents interact to drive the next generation of product innovation, operational scalability, and market differentiation.