Sr Data Analyst-Agentic AI & GenAI Delivery

Why GM Financial Technology

Innovation isn’t just a talking point at GM Financial, it’s how we operate. From generative AI and cloud-native technologies to peer-led learning and hackathons, our tech teams are building real solutions that make a difference. We’re committed to AI-powered transformation, using advanced machine learning and automation to help us reimagine customer interactions and modernize operations, positioning GM Financial as a leader in digital innovation within a dynamic industry.

Join us and discover a workplace where your ideas matter, your development is prioritized, and you can truly make a global impact.

About the role: 

The Senior Data Analyst – Agentic AI & GenAI Delivery plays a critical role in operationalizing and scaling Agentic AI solutions across the enterprise. This role focuses on driving delivery, deployment validation, and continuous optimization of AI systems through data-driven insights, validation frameworks, and reporting mechanisms.Unlike traditional data analyst roles, this position operates at the intersection of AI systems, production delivery, and performance analytics, ensuring that Agentic AI solutions are functioning as intended, meeting business objectives, and operating reliably in production environments.

This role partners closely with architects, AI engineers, product teams, and business stakeholders to:

• Validate that AI use cases align with real-world outcomes.
• Monitor agent behavior, performance, and reliability.
• Establish data-driven feedback loops for continuous improvement.

The ideal candidate brings strong expertise in data analysis, AI system validation, observability, and reporting, along with a solid understanding of Agentic AI / GenAI workflows and production deployment challenges.

In this role you will: 

• Drive the delivery and operational validation of Agentic AI solutions through structured data analysis and reporting.
• Define and implement data-driven validation frameworks to evaluate AI system performance, accuracy, reliability, and business impact.
• Analyze production data from AI systems (agents, workflows, prompts, responses) to identify trends, issues, and optimization opportunities.
• Develop dashboards, reports, and metrics to track the health and effectiveness of Agentic AI deployments.
• Partner with architecture and engineering teams to validate feasibility outcomes and ensure solutions align with real-world system behavior.
• Monitor AI systems in production, identifying anomalies, failure patterns, hallucinations, and performance degradation.
• Support deployment efforts by validating readiness criteria, including performance thresholds, guardrails, and compliance requirements.
• Enable continuous improvement loops by feeding insights back into model tuning, prompt design, and system architecture.
• Support A/B testing and experimentation for AI workflows and use cases.
• Collaborate with business stakeholders to measure and report on AI-driven business outcomes and ROI.
• Ensure transparency and traceability of AI decisions through structured logging, trace analysis, and reporting.
• Contribute to the development of AI observability frameworks, including metrics, KPIs, and alerting strategies.